Miracle-dz/UniFAR-FTUs huggingface dataset:
INS/GPS integration using neural networks for land vehicular navigation applications

Most of the positioning technologies for modern land vehicular navigation systems have been available for 25 years. Virtually all of the systems augment two or more of these technologies. Typical candidates for an integrated navigation system are the Global Position System (GPS) and Inertial Navigation Systems (INS). The Kalman filter has been widely adopted as an optimal estimation tool for the INS/GPS integration, however, several limitations of such multi-sensor integration methodology have been reported; such as the impact of INS short term errors, model dependency, prior knowledge dependency, sensor dependency, and linearization dependency. To reduce the impact of short term INS sensor errors, the bandwidth of true motion dynamics were identified by spectrum analysis and the first generation denoising algorithm that used the Discrete Wavelet Transform (DWT) was applied to identify the limitations of the existing denoising algorithm. Consequently, this research proposed the cascade denoising algorithm to overcome the limitations of existing denoising algorithms. It was then evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior performance to existing denoising algorithms in both the positioning and spectrum domains. In addition, the impact of proposed algorithms on different integrated systems was investigated extensively. Furthermore, an alternative INS/GPS integration methodology, the conceptual intelligent navigator incorporating artificial intelligence techniques, was proposed to reduce the remaining limitations of traditional navigators that use the Kalman filter approach. The proposed conceptual intelligent navigator consisted of several different INS/GPS integration architectures that were developed using artificial neural networks to acquire the navigation knowledge. In addition, the “brain”, a navigation information database, and a window based weight updating scheme were implemented to store and accumulate navigation knowledge. The conceptual intelligent navigator was evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior iii performance to traditional navigator in the position domain. Finally, a low cost INS/GPS integrated system was considered to verify the advantages gained by incorporating the conceptual intelligent navigator as an alternative method toward developing next generation land vehicular navigation systems.
Design and Evaluation of a Linear Haptic Device

The commercial development of haptic devices is very promising. Existing systems are often 6-degree-of-freedom mechanisms equipped with a stylus that acts as a tool. They exhibit force feedback for 3 or 6 degrees of freedom of their end-effector, depending on whether only forces or both forces and torques are rendered. Some planar devices are also used but oddly, one-degree-of-freedom linear haptic devices are quite rare. This lack can probably be explained by the necessary mechanical transformations that are required to achieve linear motions with rotary motors. In this paper, we review several possible structures and present the design of a new one-degree-of-freedom linear haptic device with a limited number of joints and a compact design, compatible with rotary actuation. We evaluate this device in the telemanipulation context.
Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions

[1] A Monte Carlo (MC) probabilistic approach is used to estimate uncertainties in the emissions outputs of the Biogenics Emission Inventory System Version 3 (BEIS3) model and subsequent ozone outputs of three Chemical Transport Models (CTMs) due to uncertainties in many key BEIS3 biogenics emissions model parameters and inputs. BEIS3 was developed by the Environmental Protection Agency to estimate emissions of biogenic substances such as isoprene, monoterpenes, oxygenated and other volatile organic compounds (OVOCs), and biogenic nitric oxide (BNO). Uncertainties are investigated for three time periods, 24–29 May, 11–15 July, and 4–9 September 1995, which are ozone episodes that have been extensively studied by others as part of emissions control planning exercises. In the MC approach, 1000 samples are drawn randomly and independently from seventeen BEIS3 parameters and inputs, whose distribution shapes and variances are determined from literature reviews. The 95% confidence range on the calculated uncertainties in isoprene and BNO emissions cover approximately an order of magnitude. On the other hand, the 95% confidence ranges on the calculated uncertainties in monoterpenes and OVOC emissions are much smaller: about ±20%. Correlations are calculated between the 1000 MC samples of pairs of variations in model parameters or inputs and variations in BEIS3 daily emissions estimates for individual grid squares. A few significant correlations are found for some of the assumed model parameters. The MC uncertainties in the CTM-predicted 1- and 8-hour averaged ozone concentrations were studied by drawing 20 random samples from the 1000 sets of BEIS3 outputs and running each CTM (MAQSIP, UAM-V, and URM) 20 times for the three episodes. The estimated total uncertainties of ±15 to 20% are found to be nearly the same for the three CTMs over the three time periods, for 1- and 8-hour averages.
Deep Sequencing Analysis of Small Noncoding RNA and mRNA Targets of the Global Post-Transcriptional Regulator, Hfq

Recent advances in high-throughput pyrosequencing (HTPS) technology now allow a thorough analysis of RNA bound to cellular proteins, and, therefore, of post-transcriptional regulons. We used HTPS to discover the Salmonella RNAs that are targeted by the common bacterial Sm-like protein, Hfq. Initial transcriptomic analysis revealed that Hfq controls the expression of almost a fifth of all Salmonella genes, including several horizontally acquired pathogenicity islands (SPI-1, -2, -4, -5), two sigma factor regulons, and the flagellar gene cascade. Subsequent HTPS analysis of 350,000 cDNAs, derived from RNA co-immunoprecipitation (coIP) with epitope-tagged Hfq or control coIP, identified 727 mRNAs that are Hfq-bound in vivo. The cDNA analysis discovered new, small noncoding RNAs (sRNAs) and more than doubled the number of sRNAs known to be expressed in Salmonella to 64; about half of these are associated with Hfq. Our analysis explained aspects of the pleiotropic effects of Hfq loss-of-function. Specifically, we found that the mRNAs of hilD (master regulator of the SPI-1 invasion genes) and flhDC (flagellar master regulator) were bound by Hfq. We predicted that defective SPI-1 secretion and flagellar phenotypes of the hfq mutant would be rescued by overexpression of HilD and FlhDC, and we proved this to be correct. The combination of epitope-tagging and HTPS of immunoprecipitated RNA detected the expression of many intergenic chromosomal regions of Salmonella. Our approach overcomes the limited availability of high-density microarrays that have impeded expression-based sRNA discovery in microorganisms. We present a generic strategy that is ideal for the systems-level analysis of the post-transcriptional regulons of RNA-binding proteins and for sRNA discovery in a wide range of bacteria.
Allatostatin‐like immunoreactivity in the stomatogastric nervous system and the pericardial organs of the crab Cancer pagurus, the lobster Homarus americanus, and the crayfish Cherax destructor and Procambarus clarkii

The distribution of allatostatin (AST)‐like immunoreactivity was studied in the stomatogastric nervous system (STNS) and the neurosecretory pericardial organs (PO) of four decapod crustacean species by using wholemount immunocytochemical techniques and confocal microscopy. AST‐like immunoreactivity was found within the STNS of all four species; its distribution in each was unique. In all four species, AST‐like immunoreactivity was present in the paired commissural ganglia (CoG), in the esophageal ganglion (OG), in the stomatogastric ganglion (STG), and in their connecting nerves. Within the CoGs, numerous cell bodies and neuropil were stained. In the OG, two cell bodies were immunoreactive, although their branching pattern varies between species. In the STG of C. pagurus and H. americanus, neuropil was stained extensively, but no labeled cell bodies were found. Surprisingly, in C. destructor and P. clarkii, cell bodies were stained in the STG, one brightly stained cell body in both species and an additional two to five weakly stained cell bodies in P. clarkii. In all four species, stained gastropyloric receptor cells were present. In contrast to the variable staining within the STNS, all four species have a similar pattern of AST‐like immunoreactivity within the PO. Only in C. destructor, AST‐immunoreactive varicosities occur on the surface of the circumesophageal connectives and on the postesophageal commissure and suggest another neurohaemal source for AST‐like peptides in this species. The pattern of this staining suggests that AST‐like peptides are likely utilized as both neurohormones and as neuromodulators in the STNS of decapod crustacea. J. Comp. Neurol. 403:85–105, 1999. © 1999 Wiley‐Liss, Inc.
Leptospirosis: pathogenesis, immunity, and diagnosis

Purpose of review Leptospirosis is among the most important zoonotic diseases worldwide. Completion of the genomic sequences of leptospires has facilitated advances in diagnosis and prevention of the disease, and yielded insight into its pathogenesis. This article reviews this research, emphasizing recent progress. Recent findings Leptospirosis is caused by a group of highly invasive spiral bacteria (spirochetes) that can infect both people and animals. Spirochetes can survive in the environment and host, and therefore outer membrane and secretory proteins that interact with the host are of considerable interest in leptospire research. The genetic approach to studying pathogenesis is hindered by fastidious growth of pathogenic leptospires. Integrated genomic and proteomic approaches, however, have yielded enhanced understanding of the pathogenesis of leptospirosis. Furthermore, studies of innate immune response to the organism have enhanced our understanding of host susceptibility and resistance to infection. In-silico analysis and high-throughput cloning and expression have had major impacts on efforts to develop vaccine candidates and diagnostic reagents. Summary In the future, we must effectively utilize the wealth of genetic information to combat the disease. More studies into genetics, immune mechanisms that may be exploited to prevent leptospirosis, and pathogenesis of the disease are necessary.
Polygenic Epidemiology

Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.
Mapping Different Rhetorical Relation Annotations: A Proposal

Annotation efforts have resulted in the availability of a number of corpora with rhetorical relation information. The corpora, unfortunately, are annotated under different theoretical approaches and have different hierarchie of relations. In addition, new sets of rhetorical relations have been proposed to accounfor language variation. The types of relations, however, tend to overlap or be related in specific ways. We believe that differences across approaches are minimal, and a unified set of relations that works across languages is possible. This paper details a new taxonomy of relations organized in four top level-classes with a total of 26 relations. We propose a mapping between existing annotations and show that our taxonomy is robust across theories, and can be applied to multiple languages.
The Evolution of Clinical Functional Imaging during the Past 2 Decades and Its Current Impact on Neurosurgical Planning

SUMMARY: BOLD fMRI has, during the past decade, made a major transition from a purely research imaging technique to a viable clinical technique used primarily for presurgical planning in patients with brain tumors and other resectable brain lesions. This review article briefly examines the history and evolution of clinical functional imaging, with particular emphasis on how the use of BOLD fMRI for neurosurgical planning has changed during the past 2 decades. Even more important, this article describes the many published studies during that same period that have examined the overall clinical impact that BOLD and DTI have made on surgical planning.
GM vs biharmonic ocean mixing in the Arctic

As part of the Arctic Ocean Model Intercomparison Project, the LANL ice-ocean modeling team completed two 55-year, global, ice-ocean simulations forced with atmospheric reanalysis data for 1948-2002. These two simulations differ only in the parameterization used for lateral mixing of tracers (potential temperature and salinity) in the ocean, but the resulting circulation and kinetic energy of the simulated oceans are very different, particularly at high latitudes. The differences can be traced to two effects, (1) scale selectivity, in which the Laplacian form of the Gent and McWilliams (GM) parameterization damps wave energy more quickly than the biharmonic mixing formulation, and (2) grid dependence of the diffusion coeficient, which appears in the biharmonic formulation but not in GM, and is particularly important at high latitudes where the grid scale decreases dramatically on the sphere. We conclude that, in order to maintain consistent suppression of numerical noise while allowing for a more energetic circulation in regions of finer grid spacing, future global simulations using the GM parameterization should include a diffusivity scaling factor given by the square root of the grid cell area.
Testing Working Memory: An Experimental and Analytical Approach

The following paper is basically a brief review of my studies in the concentration of cognition under the guidance of Dr. Mahadevan. In addition to demonstrating laboratory procedures, Dr. Mahadevan recommended many of the subsequent references to literature pertaining to various memory constructs. Having acquired a broad taste (but admitted novice skill) for many interpretations, clarifications by and conversations with Dr. Mahadevan have enabled me to document some conclusions here. My hope is to convey some understanding of the history, methods, theories and models I have found to be instrumental to the present-day empirical study of memory.
Donor/recipient enhancement of memory in rat hippocampus

The critical role of the mammalian hippocampus in the formation, translation and retrieval of memory has been documented over many decades. There are many theories of how the hippocampus operates to encode events and a precise mechanism was recently identified in rats performing a short-term memory task which demonstrated that successful information encoding was promoted via specific patterns of activity generated within ensembles of hippocampal neurons. In the study presented here, these “representations” were extracted via a customized non-linear multi-input multi-output (MIMO) mathematical model which allowed prediction of successful performance on specific trials within the testing session. A unique feature of this characterization was demonstrated when successful information encoding patterns were derived online from well-trained “donor” animals during difficult long-delay trials and delivered via online electrical stimulation to synchronously tested naïve “recipient” animals never before exposed to the delay feature of the task. By transferring such model-derived trained (donor) animal hippocampal firing patterns via stimulation to coupled naïve recipient animals, their task performance was facilitated in a direct “donor-recipient” manner. This provides the basis for utilizing extracted appropriate neural information from one brain to induce, recover, or enhance memory related processing in the brain of another subject.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices

Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years. However, the existing SRC type methods apply only to vector data in Euclidean space. As such, there is still no satisfactory approach to conduct classification task for symmetric positive definite (SPD) matrices which is very useful in computer vision. To address this problem, in this paper, a neighborhood preserved kernel SRC method is proposed on SPD manifolds. Specifically, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), the proposed method can perform classification on SPD manifolds through an appropriate Log-Euclidean kernel. Through exploiting the geodesic distance between SPD matrices, our method can effectively characterize the intrinsic local Riemannian geometry within data so as to well unravel the underlying sub-manifold structure. Despite its simplicity, experimental results on several famous database demonstrate that the proposed method achieves better classification results than the state-of-the-art approaches.
Attentional Filter Training but Not Memory Training Improves Decision-Making

Decision-making has a high practical relevance for daily performance. Its relation to other cognitive abilities such as executive control and memory is not fully understood. Here we asked whether training of either attentional filtering or memory storage would influence decision-making as indexed by repetitive assessments of the Iowa Gambling Task (IGT). The IGT was developed to assess and simulate real-life decision-making (Bechara et al., 2005). In this task, participants gain or lose money by developing advantageous or disadvantageous decision strategies. On five consecutive days we trained 29 healthy young adults (20–30 years) either in working memory (WM) storage or attentional filtering and measured their IGT scores after each training session. During memory training (MT) subjects performed a computerized delayed match-to-sample task where two displays of bars were presented in succession. During filter training (FT) participants had to indicate whether two simultaneously presented displays of bars matched or not. Whereas in MT the relevant target stimuli stood alone, in FT the targets were embedded within irrelevant distractors (bars in a different color). All subjects within each group improved their performance in the trained cognitive task. For the IGT, we observed an increase over time in the amount of money gained in the FT group only. Decision-making seems to be influenced more by training to filter out irrelevant distractors than by training to store items in WM. Selective attention could be responsible for the previously noted relationship between IGT performance and WM and is therefore more important for enhancing efficiency in decision-making.
splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel Computation for NLP Applications

We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.
A GA trained ANN model for maximum power point tracking in solar photo voltaic cell

Solar energy is one of the major component of renewable energy resources and maximizing the utilization of solar energy is a technically challenging task. Solar PV cells shows non liner behavior and their performance is influenced by a variety of factors. Varying insolation and temperature plays a crucial role in defining the operating point of the PV cell. Maximum Power Point Tracking (MPPT) algorithms are necessary to maximize the output power. The objective of obtaining MPP in PV systems is to regulate the actual operating voltage of PV panels. In this paper a Genetic Algorithm (GA)-Artificial Neural Network (ANN) MPPT approach is presented. GA is employed to adaptively change the weights during the course of ANN training. The proposed approach is validated by comparing its performance against a data set which contains voltages forecast by ANN approach, reference voltage obtained through mathematical modeling. The results have shown that the proposed approach is suitable for maximum power point tracking when compared to ANN. Pearson product-moment correlation has also been calculated to compare the correlation between the anticipated voltage and the reference voltage.
Angular domain reconstruction of dynamic 3D fluid surfaces

We present a novel and simple computational imaging solution to robustly and accurately recover 3D dynamic fluid surfaces. Traditional specular surface reconstruction schemes place special patterns (checkerboard or color patterns) beneath the fluid surface to establish point-pixel correspondences. However, point-pixel correspondences alone are insufficient to recover surface normal or height and they rely on additional constraints to resolve the ambiguity. In this paper, we exploit using Bokode - a computational optical device that emulates a pinhole projector - for capturing ray-ray correspondences which can then be used to directly recover the surface normals. We further develop a robust feature matching algorithm based on the Active-Appearance Model to robustly establishing ray-ray correspondences. Our solution results in an angularly sampled normal field and we derive a new angular-domain surface integration scheme to recover the surface from the normal fields. Specifically, we reformulate the problem as an over-constrained linear system under spherical coordinate and solve it using Singular Value Decomposition. Experiments results on real and synthetic surfaces demonstrate that our approach is robust and accurate, and is easier to implement than state-of-the-art multi-camera based approaches.
Group Insert Tree with Projection for Handling Vehicle Information Effectively in LBS

The dynamic advances of wireless telecommunication and positioning technologies lead to develop the moving object management system and provide suitable services according to the vehicle locations. To provide these location based services smoothly, it is necessary for an index technique to store and search lots of moving object data rapidly. However, most of the existing bulk loading techniques of moving object indices have not good search performance, because they focus on the effective insert manner. To solve this problem, we propose a GIP(Group Insert tree with Projection) in this paper. The proposed GIP reduces the input cost effectively since the manner stores the moving object location data in a group. Besides, it also reduces the search cost with Projection storage. Then we confirm the effect of the GIP which reduces the insert cost and improves the search performance in a time slice query from the experiment to compare the operation with some existing indices. The proposed GIP would be useful in the environments that require frequent insert as well as search of location information such as a transportation vehicle management and a tour-guide system.