Miracle-dz/UniFAR-FTUs huggingface dataset:
Using nuclear data and Monte Carlo techniques to study areal density and mix in D2 implosions

Measurements from three classes of direct-drive implosions at the OMEGA laser system [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)] were combined with Monte Carlo simulations to investigate models for determining hot-fuel areal density (ρRhot) in compressed, D2-filled capsules, and to assess the impact of mix and other factors on the determination of ρRhot. The results of the Monte Carlo simulations were compared to predictions of simple, commonly used models that use ratios of either secondary D3He proton yields or secondary DT neutron yields to primary DD neutron yields to provide estimates ρRhot,2p or ρRhot,2n, respectively, for ρRhot. For the first class of implosion, where ρRhot is low (⩽3 mg/cm2), ρRhot,2p and ρRhot,2n often agree with each other and are often good estimates of the actual ρRhot. For the second class of implosion, where ρRhot is of order 10 mg/cm2, ρRhot,2p often underestimates the actual value due to secondary proton yield saturation; in addition, fuel-shell mix causes ρRhot,2p.
Highly accurate dispersion relation preserving schemes for incompressible flows

The results of a 3D DNS code based on explicit optimized DRP [1] are analyzed for moderate Reynolds number (Re=4200). The aim is to show the ability of these schemes to give accurate results in a small amount of time comparing to others accurate schemes as compact schemes. To this end, we consider a wall-bounded turbulent flow. Periodic boundaries conditions are applied in the streamwise and spanwise directions combined with Dirichlet boundaries conditions.
OUTPUT FEEDBACK STABILIZATION OF NONLINEAR SYSTEMS WITH UNCERTAIN INTEGRATORS

Abstract: We present a control design to solve the global output feedback stabilization problem of a class of uncertain nonlinear systems. Each system in this class consists of some dynamic uncertainties, linear parametric uncertainties and, most importantly, output-dependent uncertain integrator gains or virtual coefficients. To solve the problem, an observer design is proposed, and the robust adaptive backstepping and the nonlinear small gain techniques are applied to construct an output feedback dynamic controller. The controller guarantees that all the closed-loop signals are bounded and also the output of the systems can be made arbitrarily small. If extra conditions are imposed on the systems, regulation of the systems’ states can also be achieved.
Single Neurons in the Monkey Hippocampus and Learning of New Associations

The medial temporal lobe is crucial for the ability to learn and retain new declarative memories. This form of memory includes the ability to quickly establish novel associations between unrelated items. To better understand the patterns of neural activity during associative memory formation, we recorded the activity of hippocampal neurons of macaque monkeys as they learned new associations. Hippocampal neurons signaled learning by changing their stimulus-selective response properties. This change in the pattern of selective neural activity occurred before, at the same time as, or after learning, which suggests that these neurons are involved in the initial formation of new associative memories.
Dynamics of low capillary number interfaces moving through sharp features

The success of any nanoimprint process depends upon its ability to exactly reproduce the template pattern. Thus, complete filling of recessed features in the template is an important issue that is controlled by the dynamics of the flow through these sharp structures. At these small scales, capillary forces are large and must be included in the fluid flow model. The mechanism of interface advancement at low capillary number through sharp rectangular features is useful for understanding how and why features fill or trap air. In this study we present a two-dimensional simulation of this feature filling to capture the details of the process, including the viscous and capillary effects. Fluid is injected into the channel between the template and substrate, where the fluid–air interface soon encounters a rectangular feature with some height greater than the channel gap. As the fluid advances through the channel, the shape of the interface is a circular arc due to the strong capillary forces. The interface maint.
Weakly Chaotic Population Dynamics in Random Ecological Networks

Population dynamics in random ecological networks are investigated by analyzing a simple deterministic equation. It is found that a sequence of abrupt changes of populations punctuating quiescent states characterize the long time behavior. An asymptotic analysis is developed by introducing a log-scaled time, and it is shown that such a dynamical process behaves as non-steady weak chaos in which population disturbances grow algebraically in time. Also, some relevance of our study to taxonomic data of biological extinction is mentioned.
Generating Feasible Test Paths from an Executable Model Using a Multi-objective Approach

Search-based testing techniques using meta-heuristics, like evolutionary algorithms, has been largely used for test data generation, but most approaches were proposed for white-box testing. In this paper we present an evolutionary approach for test sequence generation from a behavior model, in particular, Extended Finite State Machine. An open problem is the production of infeasible paths, as these should be detected and discarded manually. To circumvent this problem, we use an executable model to obtain feasible paths dynamically. An evolutionary algorithm is used to search for solutions that cover a given test purpose, which is a transition of interest. The target transition is used as a criterion to get slicing information, in this way, helping to identify the parts of the model that affect the test purpose. We also present a multi-objective search: the test purpose coverage and the sequence size minimization, as longer sequences require more effort to be executed.
Method for Building Sentence-Aligned Corpus from Wikipedia

We propose the framework of a Machine Translation (MT) bootstrapping method by using multilingual Wikipedia articles. This novel method can simultaneously generate a statistical machine translation (SMT) and a sentence-aligned corpus. In this study, we perform two types of experiments. The aim of the first type of experiments is to verify the sentence alignment performance by comparing the proposed method with a conventional sentence alignment approach. For the first type of experiments, we use JENAAD, which is a sentence-aligned corpus built by the conventional sentence alignment method. The second type of experiments uses actual English and Japanese Wikipedia articles for sentence alignment. The result of the first type of experiments shows that the performance of the proposed method is comparable to that of the conventional sentence alignment method. Additionally, the second type of experiments shows that we can obtain the English translation of 10% of Japanese sentences while maintaining high alignment quality (rank-A ratio of over 0.8).
A Novel Product Features Categorize Method Based on Twice-Clustering

Recently, the number of freely available online reviews is increasing in a high speed. More and more aspect base dopinion mining technique has been employed to find out customers' opinions. In this paper, we only focus on categorize product features that the customers have commented on. An unsupervised twice-clustering based product features categorization method is proposed. Opinion words in context of product features are chosen to represent the interrelationship among product features instead of full context information. The cluster result of active product features is used as constraints to improve the whole categorization quality. Our experimental results show that opinion words in context and their group information are very important features in measuring the semantic similarity of their associated product features. The twice-clustering strategy achieves better performance than single-clustering method.
On turbo equalization of interleaved space-time codes

Space-time coding over frequency selective time-varying fading channels is considered. The approach is based on deploying bit/symbol interleaved space-time codes. Decoding is based on the iterative concatenation of a soft-in soft-out MIMO equalizer with a soft-in soft-out decoder. The equalizer structure comprises a bank of matched filters followed by a maximum a posteriori processor with an appropriate metric. It does not require multiple receive antennas. Methods to reduce its complexity are devised. Finally, performance results are shown for ST bit-interleaved convolutional codes designed for the EDGE air interface with two transmit-single receive antennas.
Macular Cone Abnormalities in Retinitis Pigmentosa with Preserved Central Vision Using Adaptive Optics Scanning Laser Ophthalmoscopy

Purpose To assess macular photoreceptor abnormalities in eyes with retinitis pigmentosa (RP) with preserved central vision using adaptive optics scanning laser ophthalmoscopy (AO-SLO). Methods Fourteen eyes of 14 patients with RP (best-corrected visual acuity 20/20 or better) and 12 eyes of 12 volunteers underwent a full ophthalmologic examination, fundus autofluorescence, spectral-domain optical coherence tomography (SD-OCT), and imaging with a prototype AO-SLO system. Cone density and spatial organization of the cone mosaic were assessed using AO-SLO images. Results In 3 eyes with RP and preserved central vision, cones formed a mostly regular mosaic pattern with small patchy dark areas, and in 10 eyes, the cone mosaic patterns were less regular, and large dark regions with missing cones were apparent. Only one eye with RP demonstrated a normal, regular cone mosaic pattern. In eyes with RP, cone density was significantly lower at 0.5 mm and 1.0 mm from the center of the fovea compared to normal eyes (P<0.001 and 0.021, respectively). At 0.5 mm and 1.0 mm from the center of the fovea, a decreased number of cones had 6 neighbors in eyes with RP (P = 0.002 for both). Greater decrease in cone density was related to disruption of the photoreceptor inner segment (IS) ellipsoid band on SD-OCT images (P = 0.044); however, dark regions were seen on AO-SLO even in areas of continuous IS ellipsoid on SD-OCT. Decreased cone density correlated thinner outer nuclear layer (P = 0.029) and thinner inner segment and outer segment thickness (P = 0.011) on SD-OCT. Conclusions Cone density is decreased and the regularity of the cone mosaic spatial arrangement is disrupted in eyes with RP, even when visual acuity and foveal sensitivity are good. AO-SLO imaging is a sensitive quantitative tool for detecting photoreceptor abnormalities in eyes with RP.
A Reconfigurable Hardware Accelerated Platform for Clustered Wireless Sensor Networks

With the advent of the FPGA technology, a reconfigurable platform becomes possible to enhance the capability and adaptability of wireless sensor nodes while reducing the energy consumption. In this paper, we investigate the application of reconfigurable platform in wireless sensor networks. We focus on using hardware accelerated lossless compression to reduce the energy consumption of the data aggregation in clustered networks. Our study is based on real-world measurement on our integrated reconfigurable sensor node and the measured data is then used in the network simulation with a proper channel model. Our experiments show that the use of hardware acceleration on our platform can reduce the energy consumption by up to 35%. In addition, with proper parameters of the network, the run-time reconfiguration becomes beneficial and this opens a lot of opportunities for the optimizations according to the field situations.
Learning with power l1-graph for single labeled image biometric recognition

Single labeled biometric recognition is one of the main challenges to graph-based transductive classification learning. To enhance the recognition rate of single labeled problem, sparse representation provides a feasible strategy for representation learning. In this paper, we developed a power l1-graph learning technique for semi-supervised learning, called label propagation by power l1-graph (LPPG). Different from all existing graph-based methods, we assume that the similarity relationship in the label space is a power function in the sample space. What is important is that the determinated power value measured by sparseness is given. Our method characterizes the second sparse processing, and seeks to find a reasonable label propagation way. This characteristic makes our algorithm more intuitive and more powerful than those methods based on the original l1-graph. This proposed method is applied to biometrics recognition and the experiment results show that our algorithm consistently outperforms those original l1-graph-based methods. This demonstrates that our method is a good choice for real-world biometrics applications, especially when there is only one labeled image. Key words: Power function, sparse reconstruction, label propagation, biometrics recognition.
A Focus on Student Learning: The Self-Study/School Improvement Process

School improvement is founded on collaborative work structures, shared decision making, redesigned teacher responsibilities, reallocated resources, and im­ provements in the process of teaching and learning (Murphy, 1991) . Accountability for student success presents an emerging dynamic for school improve­ ment. Lieberman (1991) observes that making schools genuinely accountable for student learning will require involving teachers in the development of methods and
Analysis of single-event effects in embedded processors for non-uniform fault tolerant design

Advances in silicon technology and shrinking the feature size to nanometer scale make unreliability of nano devices the most important concern of fault-tolerant designs. Design of reliable and fault-tolerant embedded processors is mostly based on developing techniques that compensate adding hardware or software redundancy. The recently-proposed redundancy techniques are generally applied uniformly to a system and lead to inefficiencies in terms of performance, power, and area. Non-uniform redundancy requires a quantitative analysis of the system behavior encountering transient faults. In this paper, we introduce a custom fault injection framework that helps to locate the most vulnerable nodes and components of embedded processors. Our framework is based on an exhaustive transient fault injection to candidate nodes which are selected from a user-defined list. Furthermore, the list of nodes containing the microarchitectural state is also defined by user to validate execution of instructions. Based on the reported results, the most vulnerable nodes, components, and instructions are found and could be used for an effective non-uniform fault-tolerant redundancy technique.
Constructing A Realistic Face Model of An Individual for Expression Animation

This paper presents a method for creating photorealistic textured 3D face models of specific people for dynamic facial expression animation. The modeling approach reconstructs an accurate geometrical face model based on the individual face measurements containing both shape and texture information that is acquired from a laser range scanner. By using a semi -automatic registration and merging technique, a 3D dense face mesh is recovered fro m the partial range data obtained from arbitrary multiple different views. A model editing and adaptive meshing scheme is then used to refine the surface model. Having recovered the facial geometry, we add realism by mapping the model with high-resolution texture images. The resultant synthetic face has been shown to be visually similar to the true face. Based on the geometrically accurate surface model, a physically-based face model with a hierarchical structure of the skin, muscles and skull is developed from anatomical perspective. The dynamic displacement of nodes in the skin lattice under the influence of internal muscular forces is calculated by a numerical integration method. Using our technique, we have been able to generate highly realistic face models and flexible expressions.
Prospect Theoretic Analysis of Energy Exchange Among Microgrids

The energy exchange between microgrids (MGs) that are capable of generating power from renewable energy sources in smart grids is investigated. As MGs are autonomous and have control over their energy exchange, prospect theory is a useful tool to provide a user-centric view on MG power trading. More specifically, in this paper, the energy exchange among MGs that are also connected to a power plant as a backup energy supply is formulated as a prospect theory-based static game and Nash equilibria are provided under various scenarios. The impact of user objective weight is evaluated during the outcome evaluation on the performance of the game. Simulation results show that user subjectivity tends to exaggerate selling and buying probabilities when battery levels are high (and low), and thus decreases the overall utility and increases the amount of the energy bought at either low battery levels or low MG selling prices. Conditions on the pricing system to ensure that the energy exchange system is not impacted by the subjective view of MGs are also provided.
WEB STRUCTURE MINING

I n the first part of the book we discuss basic ideas and techniques for extracting text information from the Web, including collecting and indexing web documents and searching and ranking web pages by their textual content and hyperlink structure. We first discuss the motivation to organize the web content and find better ways for web search to make the vast knowledge on the Web easily accessible. Then we describe briefly the basics of the Web and explore the approaches taken by web search engines to retrieve web pages by keyword search. To do this we look into the technology for text analysis and search developed earlier in the area of information retrieval and extended recently with ranking methods based on web hyperlink structure. All that may be seen as a preprocessing step in the overall process of data mining the web content, which provides the input to machine learning methods for extracting knowledge from hypertext data, discussed in the second part of the book.
Guoguo: Enabling Fine-Grained Smartphone Localization via Acoustic Anchors

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users' smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.
Hedge Funds and the Origins of Private Information in Currency Markets

This paper provides evidence that hedge funds are a critical source of private fundamental information in currency markets. We analyze the most disaggregated database of currency transactions to date, with ten different categories of market participants including six categories of end users. Our analysis of the information content of individual trades indicates that only one category of end user has information, specifically hedge funds. Orders placed by institutional investors, broker-dealers, central banks and government agencies, large corporations, and middle-market corporations provide little information about upcoming returns. Orders of banks in every size category carry information, consistent with now-standard theory that banks gather information from observing customer trades. Theory does not indicate whether banks should be better informed than their customers. Our results suggest that banks are better informed than their individual customers, possibly because they aggregate information from many customers.
Distributed In-Memory Processing of All k Nearest Neighbor Queries

A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this paper, we present Spitfire, a distributed algorithm that provides a scalable and high-performance AkNN processing framework. Our proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. We evaluate, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three other state-of-the-art distributed AkNN algorithms executed in distributed main-memory.
Document image zone classification - a simple high-performance approach

We describe a simple, fast, and accurate system for document image zone classification — an important subproblem of document image analysis — that results from a detailed analysis of different features. Using a novel combination of known algorithms, we achieve a very competitive error rate of 1.46% (n = 13811) in comparison to (Wang et al., 2006) who report an error rate of 1.55% (n = 24177) using more complicated techniques. The experiments were performed on zones extracted from the widely used UW-III database, which is representative of images of scanned journal pages and contains ground-truthed real-world data.