Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband RF Interferometric Mapping and Polarization (BIMAP) Observations of Lightning Discharges: Revealing New Physics Insights Into Breakdown Processes

Broadband radio frequency (RF) interferometry was first introduced in 1996 for lightning studies. The technique has since been improved significantly and has been increasingly widely used in the lightning community. In addition to the interferometry, we now introduce a broadband polarization capability that simultaneously measures the full polarization state of the corresponding RF sources. Polarization observation provides another level of understanding of the discharge processes. In this paper, we describe the new system and present example observations with such a system. Initial observations of impulsive lightning RF pulses, a K‐event leader, and the initiating process for an intracloud flash are presented. The impulsive pulses were often linearly polarized and are consistent with conventional breakdown processes, whereas some occasional pulses were elliptically polarized and are likely related to relativistic breakdown processes. The K‐event was detected polarized along a section of its channel, and the orientation of the polarization was mostly orthogonal to the channel, apparently due to the preceding charge deposition along the channel. The intracloud flash in this paper was initiated with downward fast positive streamers, and the corresponding signals were linearly polarized, consistent with conventional breakdown processes. The orientations of the polarization were nearly uniformly aligned with the propagation direction of the streamers, indicating a near uniform electric field in the region of the initiating streamers. Finally, an observation of a cosmic‐ray shower is presented.
Impact of Expanding SSI on Medicaid Expenditures of Disabled Children

Supplemental Security Income (SSI) expansions for disabled children in the early 1990s provoked criticism that eligibility criteria were too lax and motivated the subsequent retraction of benefits for many children. However, little evidence exists on whether the clinical needs of SSI children declined during this period. The authors used Medicaid data to examine changes in average expenditures between 1989 and 1992, using an Aid to Families with Dependent Children (AFDC) comparison group to control for confounding time trends (e.g., in access). Results showed declines in average expenditures in Georgia and Tennessee but increases in California and Michigan, which are thought to have started with more liberal eligibility policies.
Eurographics/ Ieee-vgtc Symposium on Visualization (2006) Pointcloudxplore: Visual Analysis of 3d Gene Expression Data Using Physical Views and Parallel Coordinates

To allow a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos. Here we report the first components of a visualization tool, PointCloudXplore, that allows the relationships between different gene's expression to be analyzed using the BDTNP's datasets. PointCloudXplore uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloud-Xplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PointCloudXplore, physical views of the data are linked to parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within the embryo. Parallel coordinates , on the other hand, show only some features of each gene's expression, but allow simultaneous analysis of data for many more genes than would be possible in a physical view. We have developed several extensions to standard parallel coordinates to facilitate brushing the visualization of 3D gene expression data.
The k1k2 space in range image analysis

Range finder images may be segmented by using a decision space H(k1, k2) where k1 represents the maximum local surface curvature and k2 the minimum surface curvature. Since clusters within H(k1, k2) represent various types of surfaces, the pixels in the range image Z(x, y) are classified accordingly. Homogeneous regions within Z(x, y) which correspond to classes in H(k1, k2) are thus automatically recognized according to their surface curvature characteristics. The advantages of using the maximum-minimum surface curvature (k1, k2) decision space for the first classification step in range image processing and understanding are demonstrated. <<ETX>>
Style machines

We approach the problem of stylistic motion synthesis by learning motion patterns from a highly varied set of motion capture sequences. Each sequence may have a distinct choreography, performed in a distinct sytle. Learning identifies common choreographic elements across sequences, the different styles in which each element is performed, and a small number of stylistic degrees of freedom which span the many variations in the dataset. The learned model can synthesize novel motion data in any interpolation or extrapolation of styles. For example, it can convert novice ballet motions into the more graceful modern dance of an expert. The model can also be driven by video, by scripts or even by noise to generate new choreography and synthesize virtual motion-capture in many styles.
Morphology and Physiology of Excitatory Neurons in Layer 6b of the Somatosensory Rat Barrel Cortex

Neocortical lamina 6B (L6B) is a largely unexplored layer with a very heterogeneous cellular composition. To date, only little is known about L6B neurons on a systematic and quantitative basis. We investigated the morphological and electrophysiological properties of excitatory L6B neurons in the rat somatosensory barrel cortex using whole-cell patch-clamp recordings and simultaneous biocytin fillings. Subsequent histological processing and computer-assisted 3D reconstructions provided the basis for a classification of excitatory L6B neurons according to their structural and functional characteristics. Three distinct clusters of excitatory L6B neurons were identified: (C1) pyramidal neurons with an apical dendrite pointing towards the pial surface, (C2) neurons with a prominent, “apical”-like dendrite not oriented towards the pia, and (C3) multipolar spiny neurons without any preferential dendritic orientation. The second group could be further subdivided into three categories termed inverted, “tangentially” oriented and “horizontally” oriented neurons. Furthermore, based on the axonal domain two subcategories of L6B pyramidal cells were identified that had either a more barrel-column confined or an extended axonal field. The classification of excitatory L6B neurons provided here may serve as a basis for future studies on the structure, function, and synaptic connectivity of L6B neurons.
Wicket Fall Concept Mining From Cricket Video Using A-Priori Algorithm

In this paper, we propose an algorithm to detect semantic concepts from cricket video. In our previous work, we have proposed key frame detection based approach for semantic event detection and classification. The proposed scheme works in two parts. In first part a top-down event detection and classification is performed using hierarchical tree. In second part, higher level concept is identified by applying A-Priori algorithm. In part 1, key frames are identified based on Hue Histogram difference at level 1. At level 2, logo transitions classify the frames as real-time or replay. At level 3, we classify the real time frames as field view, pitch view or non field view based on thresholds like Dominant Soli Pixel Ration (DSPR) and Dominant Grass Pixel Ration (DGPR). At level 4, we detect close up and crowd frames based upon edge detection. At level 5a, we classify the close up frames into player of team A, player of team B and umpire based upon skin colour and corresponding jersey colour. At level 5b, we classify the crowd frames into spectators, player’s gathering of team A or player’s gathering of team B. In part two, labels are associated with each frame event, which is used as input to A-Priori algorithm for concept mining. Results at the end of paper show the robustness of our approach.
Global ranking by exploiting user clicks

It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked in the result set during a user search session. We formulate the problem as a global ranking problem, emphasizing the importance of the sequential nature of user clicks, with the goal to predict the relevance labels of all the documents in a search session. This is distinct from conventional learning to rank methods that usually design a ranking model defined on a single document; in contrast, in our model the relational information among the documents as manifested by an aggregation of user clicks is exploited to rank all the documents jointly. In particular, we adapt several sequential supervised learning algorithms, including the conditional random field (CRF), the sliding window method and the recurrent sliding window method, to the global ranking problem. Experiments on the click data collected from a commercial search engine demonstrate that our methods can outperform the baseline models for search results re-ranking.
CLIMB grammars: three projects using metagrammar engineering

This paper introduces the CLIMB (Comparative Libraries of Implementations with Matrix Basis) methodology and grammars. The basic idea behind CLIMB is to use code generation as a general methodology for grammar development in order to create a more systematic approach to grammar development. The particular method used in this paper is closely related to the LinGO Grammar Matrix. Like the Grammar Matrix, resulting grammars are HPSG grammars that can map bidirectionally between strings and MRS representations. The main purpose of this paper is to provide insight into the process of using CLIMB for grammar development. In addition, we describe three projects that make use of this methodology or have concrete plans to adapt CLIMB in the future: CLIMB for Germanic languages, CLIMB for Slavic languages and CLIMB to combine two grammars of Mandarin Chinese. We present the first results that indicate feasibility and development time improvements for creating a medium to large coverage precision grammar.
Improved channel estimation with partial sparse constraint for AF cooperative communication systems

Accurate channel state information (CSI) is necessary for coherent detection in amplify and forward (AF) broadband cooperative communication systems. Based on the assumption of ordinary sparse channel, efficient sparse channel estimation methods have been investigated in our previous works. However, when the cooperative channel exhibits partial sparse structure rather than ordinary sparsity, our previous method cannot take advantage of the prior information. In this paper, we propose an improved channel estimation method with partial sparse constraint on cooperative channel. At first, we formulate channel estimation as a compressive sensing problem and utilize sparse decomposition theory. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over ordinary sparse channel estimation methods.
System Capacity and Coverage Analysis of Femtocell Networks

Recently much attention has been devoted to the potential of femtocell to improve indoor cellular coverage and high speed wireless communications. Femtocell networks use home base station and existing digital subscriber line (DSL) or other cable line as backhaul connectivity constructing a hierarchical cell structure (HCS) networks operating with macrocells which cover wider service areas. This paper investigates the effect of interference on system performances of femtocell embedded HCS networks adopting detailed radio propagation models while most of works evaluate system performance using simple radio propagation models. Considering various environmental factors such as wall structure, number of walls, and distance between femtocell and users, this paper evaluates not only the overall outage probability and the dynamic range of spectral efficiency achieved by overall femtocells in HCS networks, but also separation distances from macrocell required to femtocell for provision of the minimum required spectral efficiency. Our evaluation includes outage probability of 2$\sim$40\%, spectral efficiency dynamics of around 250\%, 50$\sim$200\textit{m} of required separation from macrocell with various wall structures, which could be useful for femtocell embedded HCS network planning.
What do packet dispersion techniques measure?

The packet pair technique estimates the capacity of a path (bottleneck bandwidth) from the dispersion (spacing) experienced by two back-to-back packets. We demonstrate that the dispersion of packet pairs in loaded paths follows a multimodal distribution, and discuss the queueing effects that cause the multiple modes. We show that the path capacity is often not the global mode, and so it cannot be estimated using standard statistical procedures. The effect of the size of the probing packets is also investigated, showing that the conventional wisdom of using maximum sized packet pairs is not optimal. We then study the dispersion of long packet trains. Increasing the length of the packet train reduces the measurement variance, but the estimates converge to a value, referred to as the asymptotic dispersion rate (ADR), that is lower than the capacity. We derive the effect of the cross traffic in the dispersion of long packet trains, showing that the ADR is not the available bandwidth in the path, as was assumed in previous work. Putting all the pieces together, we present a capacity estimation methodology that has been implemented in a tool called pathrate.
An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

A high-performance differential global positioning system (GPS) receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.
RELATING CONTENT AND NATURE OF INFORMATION WHEN TEACHING VOLLEYBALL IN YOUTH VOLLEYBALL TRAINING SETTINGS

The purpose of the study was to gain a deeper insight into the instructional process in youth volleyball training. A mixed method design, combining systematic observation and interview procedures, was used to examine both the nature and the content of the information provided by coaches during the instructional process. Twenty-eight young volleyball coaches, who coached amateur teams in a club setting, players from 13 to 17 years of age, were interviewed and one of their training sessions was videotaped for systematic observation. Results of interviews showed that coaches have adopted two agendas to teaching volleyball contents: a skills agenda, following a molecular approach, which focuses on the biomechanically efficient component; and a game agenda, using modified games and competition to facilitate the integration of technical skills in game situations. However, coach behaviour profiles showed a predominant technical orientation. All the volleyball contents were targeted in the coaches’ instructions of technique. The same did not occur in the coaches’ instructions of individual tactics, and even less in team tactics. The dominance of a technical perspective suggests the need to deepen youth coaching, to help coaches to improve their competence and to change and diversify their instructional approaches.
Nitric oxide-independent vasodilator rescues heme-oxidized soluble guanylate cyclase from proteosomal degradation

Nitric oxide (NO) is an essential vasodilator. In vascular diseases, oxidative stress attenuates NO signaling by both chemical scavenging of free NO and oxidation and downregulation of its major intracellular receptor, the αβ heterodimeric heme-containing soluble guanylate cyclase (sGC). Oxidation can also induce loss of the heme of sGC, as well as the responsiveness of sGC to NO. sGC activators such as BAY 58-2667 bind to oxidized/heme-free sGC and reactivate the enzyme to exert disease-specific vasodilation. Here, we show that oxidation-induced downregulation of sGC protein extends to isolated blood vessels. Mechanistically, degradation was triggered through sGC ubiquitination and proteasomal degradation. The heme-binding site ligand BAY 58-2667 prevented sGC ubiquitination and stabilized both α and β subunits. Collectively, our data establish oxidation–ubiquitination of sGC as a modulator of NO/cGMP signaling and point to a new mechanism of action for sGC activating vasodilators by stabilizing their receptor, oxidized/heme-free sGC.
An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable.
EXPLODE: a lightweight, general system for finding serious storage system errors

Storage systems such as file systems, databases, and RAID systems have a simple, basic contract: you give them data, they do not lose or corrupt it. Often they store the only copy, making its irrevocable loss almost arbitrarily bad. Unfortunately, their code is exceptionally hard to get right, since it must correctly recover from any crash at any program point, no matter how their state was smeared across volatile and persistent memory. This paper describes EXPLODE, a system that makes it easy to systematically check real storage systems for errors. It takes user-written, potentially system-specific checkers and uses them to drive a storage system into tricky corner cases, including crash recovery errors. EXPLODE uses a novel adaptation of ideas from model checking, a comprehensive, heavy-weight formal verification technique, that makes its checking more systematic (and hopefully more effective) than a pure testing approach while being just as lightweight. EXPLODE is effective. It found serious bugs in a broad range of real storage systems (without requiring source code): three version control systems, Berkeley DB, an NFS implementation, ten file systems, a RAID system, and the popular VMware GSX virtual machine. We found bugs in every system we checked, 36 bugs in total, typically with little effort.
Design and Implementation of a Short Message Service Data Channel for Mobile Systems

The Short Message Service (SMS) is one of the most ubiquitous wireless technologies on Earth. Each year hundreds of billions of messages are sent, demand continues to grow, and competition between cellular providers is driving prices down. These trends create practical opportunities for the use of SMS in mobile systems. In this paper we present the design and implementation of an SMS-based data channel, or SMS-NIC, that is lightweight and runs on multiple mobile platforms. Through integration with an existing mobile platform, we show that the SMS-NIC has little operational overhead and provides efficient, reliable transport for large messages send over the cellular network, where message losses frequently occur. We motivate the design of the SMS-NIC through a characterization of SMS using workloads consisting of bursts of messages between sender and receiver. This analysis differs from previous SMS studies by focusing on transmission patterns that differ from normal cell phone use. Through this characterization we show that message delay and message loss are affected by the transmission order, the time of day has minimal effect on transmission rate, delay, and loss, and that losses and high delays significantly outweigh the rate or message reordering.