Miracle-dz/UniFAR-FTUs huggingface dataset:
The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks

Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results with small and often negligible and bounded error. In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth, using mica2 motes, we perform a large-scale measurement of real environmental data (temperature and light, both indoor and outdoor) and the wireless radio reliability, which were used for both analytical modeling and simulation experiments. Fifth, we model the spatially correlated data using the properties of our real world measurements. Our experimental results show that when we compute the average of sensor readings in the network using the CAG interactive mode with the user-provided error threshold of, 20%, we can save 68.25% of transmissions over TAG with only 2.46% inaccuracy in the result. The streaming mode of CAG can save even more transmissions (up to 70.24% in our experiments) over TAG, when data shows high spatial and temporal correlations. We expect these results to hold in reality, because we used the mica2 radio profile and empirical datasets for our simulation study. CAG is the first system that leverages spatial and temporal correlations to improve energy efficiency of in-network aggregation. This study analytically and empirically validates CAG's effectiveness.
Discrete Fourier Analysis of Multigrid Algorithms 1

The main topic of this report is a detailed discussion of the discrete Fourier multilevel analysis of multigrid algorithms. First, a brief overview of multigrid methods is given for discretizations of both linear and nonlinear partial differential equations. Special attention is given to the hp-Multigrid as Smoother algorithm, which is a new algorithm suitable for higher order accurate discontinuous Galerkin discretizations of advection dominated flows. In order to analyze the performance of the multigrid algorithms the error transformation operator for several linear multigrid algorithms are derived. The operator norm and spectral radius of the multigrid error transformation are then computed using discrete Fourier analysis. First, the main operations in the discrete Fourier analysis are defined, including the aliasing of modes. Next, the Fourier symbol of the multigrid operators is computed and used to obtain the Fourier symbol of the multigrid error transformation operator. In the multilevel analysis, two and three level h-multigrid, both for uniformly and semi-coarsened meshes, are considered, and also the analysis of the hp-Multigrid as Smoother algorithm for three polynomial levels and three uniformly and semi-coarsened meshes. The report concludes with a discussion of the multigrid operator norm and spectral radius. In the appendix some useful auxiliary results are summarized.
Structure and Pharmacologic Modulation of Inhibitory Glycine Receptors

Glycine receptors (GlyR) are inhibitory Cys-loop ion channels that contribute to the control of excitability along the central nervous system (CNS). GlyR are found in the spinal cord and brain stem, and more recently they were reported in higher regions of the CNS such as the hippocampus and nucleus accumbens. GlyR are involved in motor coordination, respiratory rhythms, pain transmission, and sensory processing, and they are targets for relevant physiologic and pharmacologic modulators. Several studies with protein crystallography and cryoelectron microscopy have shed light on the residues and mechanisms associated with the activation, blockade, and regulation of pentameric Cys-loop ion channels at the atomic level. Initial studies conducted on the extracellular domain of acetylcholine receptors, ion channels from prokaryote homologs—Erwinia chrysanthemi ligand-gated ion channel (ELIC), Gloeobacter violaceus ligand-gated ion channel (GLIC)—and crystallized eukaryotic receptors made it possible to define the overall structure and topology of the Cys-loop receptors. For example, the determination of pentameric GlyR structures bound to glycine and strychnine have contributed to visualizing the structural changes implicated in the transition between the open and closed states of the Cys-loop receptors. In this review, we summarize how the new information obtained in functional, mutagenesis, and structural studies have contributed to a better understanding of the function and regulation of GlyR.
Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse recovery: A randomized clinical trial.

OBJECTIVE To compare the efficacy of Mindfulness-Based Addiction Treatment (MBAT) to a Cognitive Behavioral Treatment (CBT) that matched MBAT on treatment contact time, and a Usual Care (UC) condition that comprised brief individual counseling. METHOD Participants (N = 412) were 48.2% African American, 41.5% non-Latino White, 5.4% Latino, and 4.9% other, and 57.6% reported a total annual household income < $30,000. The majority of participants were female (54.9%). Mean cigarettes per day was 19.9 (SD = 10.1). Following the baseline visit, participants were randomized to UC (n = 103), CBT (n = 155), or MBAT (n = 154). All participants were given self-help materials and nicotine patch therapy. CBT and MBAT groups received 8 2-hr in-person group counseling sessions. UC participants received 4 brief individual counseling sessions. Biochemically verified smoking abstinence was assessed 4 and 26 weeks after the quit date. RESULTS Logistic random effects model analyses over time indicated no overall significant treatment effects (completers only: F(2, 236) = 0.29, p = . 749; intent-to-treat: F(2, 401) = 0.9, p = . 407). Among participants classified as smoking at the last treatment session, analyses examining the recovery of abstinence revealed a significant overall treatment effect, F(2, 103) = 4.41, p = . 015 (MBAT vs. CBT: OR = 4.94, 95% CI: 1.47 to 16.59, p = .010, Effect Size = .88; MBAT vs. UC: OR = 4.18, 95% CI: 1.04 to 16.75, p = .043, Effect Size = .79). CONCLUSION Although there were no overall significant effects of treatment on abstinence, MBAT may be more effective than CBT or UC in promoting recovery from lapses. (PsycINFO Database Record
SXT-Related Integrating Conjugative Element in New World Vibrio cholerae

ABSTRACT SXT-related integrating conjugative elements (ICEs) became prevalent in Asian Vibrio cholerae populations after V. cholerae O139 emerged. Here, we describe an SXT-related ICE, ICEVchMex1, in a Mexican environmental V. cholerae isolate. Identification of ICEVchMex1 represents the first description of an SXT-related ICE in the Western Hemisphere. The significant differences between the SXT and ICEVchMex1 genomes suggest that these ICEs have evolved independently.
Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM

Given the difficulty of experimental determination of drug-protein interactions, there is a significant motivation to develop effective in silico prediction methods that can provide both new predictions for experimental verification and supporting evidence for experimental results. Most recently, classification methods such as support vector machines (SVMs) have been applied to drug-target prediction. Unfortunately, these methods generally rely on measures of the maximum "local similarity" between two protein sequences, which could mask important drug-protein interaction information since drugs are much smaller molecules than proteins and drug-target binding regions must comprise only small local regions of the proteins. We therefore develop a novel sparse learning method that considers sets of short peptides. Our method integrates feature selection, multi-instance learning, and Gaussian kernelization into an L(1) norm support vector machine classifier. Experimental results show that it not only outperformed the previous methods but also pointed to an optimal subset of potential binding regions. Supplementary materials are available at "www.cs. ualberta.ca/~ys3/drug_target".
Optimization of hydrodynamic and hydrostatic steering control system based on GA-PID

As the tracked vehicles often run in harsh environments, a certain type of tracked vehicles with hydrodynamic and hydrostatic steering mechanism often have a big pressure fluctuation in the hydrostatic system in the steering process and the steering properties are seriously affected. In order to smoothly match the hydrodynamic and hydrostatic steering mechanism, we implemented the electro-hydraulic proportional valves instead of the oil-filling valves that equipped in the real vehicle and modeled the steering system in the Matlab/Simulink platform in this paper. A PID controller that optimized by the Generic Algorithm (GA) can control the oil flow into the hydraulic equipped according to the fluctuation of pressure was designed. The simulation results showed that the optimized system inhibit the pressure fluctuation well, with a rapid dynamic response and good steering performance.
High-speed autonomous navigation of unknown environments using learned probabilities of collision

We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a map in real time from onboard sensor data. Whereas existing planners for exploration confine themselves to a conservative set of constraints to guarantee safety around unknown regions of the environment, we instead learn a hazard function from data, which maps the vehicle's dynamic state and current environment knowledge to a probability of collision. We perform receding horizon planning in which the objective function is evaluated in expectation over those learned probabilities of collision. Our algorithm demonstrates sensible emergent behaviors, like swinging wide around blind corners, slowing down near the map frontier, and accelerating in regions of high visibility. Our algorithm is capable of navigating from start to goal much more quickly than the conservative baseline planner without sacrificing safety. We demonstrate our algorithm on a 1:8-scale high-performance RC car equipped with a planar laser range-finder and inertial measurement unit, reaching speeds of 4m/s in unknown, indoor spaces. A video of experimental results is available at: http: //groups.csail. mit.edu/rrg/nav_learned_prob_collision.
Predictive client-side profiles for personalized advertising

Personalization is ubiquitous in modern online applications as it provides significant improvements in user experience by adapting it to inferred user preferences. However, there are increasing concerns related to issues of privacy and control of the user data that is aggregated by online systems to power personalized experiences. These concerns are particularly significant for user profile aggregation in online advertising. This paper describes a practical, learning-driven client-side personalization approach for keyword advertising platforms, an emerging application previously not addressed in literature. Our approach relies on storing user-specific information entirely within the user's control (in a browser cookie or browser local storage), thus allowing the user to view, edit or purge it at any time (e.g., via a dedicated webpage). We develop a principled, utility-based formulation for the problem of iteratively updating user profiles stored client-side, which relies on calibrated prediction of future user activity. While optimal profile construction is NP-hard for pay-per-click advertising with bid increments, it can be efficiently solved via a greedy approximation algorithm guaranteed to provide a near-optimal solution due to the fact that keyword profile utility is submodular: it exhibits the property of diminishing returns with increasing profile size. We empirically evaluate client-side keyword profiles for keyword advertising on a large-scale dataset from a major search engine. Experiments demonstrate that predictive client-side personalization allows ad platforms to retain almost all of the revenue gains from personalization even if they give users the freedom to opt out of behavior tracking backed by server-side storage. Additionally, we show that advertisers can potentially increase their return on investment significantly by utilizing bid increments for keyword profiles in their ad campaigns.
MODIS BRDF / Albedo Product : Algorithm Theoretical Basis Document Version 5 . 0

The MODIS BRDF/Albedo product combines registered, multidate, multiband, atmospherically corrected surface re ectance data from the MODIS and MISR instruments to t a Bidirectional Re ectance Distribution Function (BRDF) in seven spectral bands at a 1 km spatial resolution on a 16-day cycle. From this characterization of the surface anisotropy, the algorithm performs angular integrations to derive intrinsic land surface albedos for each spectral band and three broad bands covering the solar spectrum. The albedo measures are a directional hemispherical re ectance (black sky albedo) obtained by integrating the BRDF over the exitance hemisphere for a single irradiance direction, and a bihemispherical re ectance (white sky albedo) obtained by integrating the BRDF over all viewing and irradiance directions. Because these albedo measures are purely properties of the surface and do not depend on the state of the atmosphere, they can be used with any atmospheric speci cation to provide true surface albedo as an input to regional and global climate models. The at-launch algorithm makes use of a linear kernel-based model { the semiempirical reciprocal RossThick-LiSparse model. Validation of the BRDF model and its performance under conditions of sparse angular sampling and noisy re ectances shows that the retrievals obtained are generally reliable. The solar-zenith angle dependence of albedo may be parameterized by a simple polynomial that makes it unnecessary for the user to be familiar with the underlying BRDF model. Spectral-to-broadband conversion is achieved using banddependent weighting factors. The intrinsic land surface albedos may be used to derive actual albedo by taking into account the prevailing distribution of di use skylight. Albedo is a fundamental parameter for climate modeling, since it is a property that drives much of the energy ux at the land boundary layer. Global maps of land surface albedo, which can be provided at ne to coarse scales using the BRDF/Albedo Product, will be extremely useful to the global and regional climate modeling communities.
Query-based data pricing

Data is increasingly being bought and sold online, and Web-based marketplace services have emerged to facilitate these activities. However, current mechanisms for pricing data are very simple: buyers can choose only from a set of explicit views, each with a specific price. In this paper, we propose a framework for pricing data on the Internet that, given the price of a few views, allows the price of any query to be derived automatically. We call this capability "query-based pricing. " We first identify two important properties that the pricing function must satisfy, called arbitrage-free and discount-free. Then, we prove that there exists a unique function that satisfies these properties and extends the seller's explicit prices to all queries. When both the views and the query are Unions of Conjunctive Queries, the complexity of computing the price is high. To ensure tractability, we restrict the explicit prices to be defined only on selection views (which is the common practice today). We give an algorithm with polynomial time data complexity for computing the price of any chain query by reducing the problem to network flow. Furthermore, we completely characterize the class of Conjunctive Queries without self-joins that have PTIME data complexity (this class is slightly larger than chain queries), and prove that pricing all other queries is NP-complete, thus establishing a dichotomy on the complexity of the pricing problem when all views are selection queries.
Towards speech recognition oriented dereverberation

We show the effect of reverberation on the speech recognition performance in a far-field microphone. Given the reference of a close-talk microphone signal, an improvement is shown using an asymmetric non-causal inverse filter both in a synthetic and real room environment. Its variants in time and frequency domain are also presented and compared with other existing techniques. We argue for the approaches which specifically consider recognition performance as a goal in deriving the dereverberating schemes, with evaluation on real room recording recognition, as a future direction for solving reverberation problems in speech recognition.
MRI brain tumor classification using Support Vector Machines and meta-heuristic method

We present a development of a new approach for automated diagnosis, based on classification of Magnetic Resonance (MR) human brain images. 2D Wavelet Transform and Spatial Gray Level Dependence Matrix (DWT-SGLDM) is used for feature extraction. For feature selection Simulated Annealing (SA) is applied to reduce features size. The next step in our approach is Stratified K-fold Cross Validation to avoid overfitting. To optimize support vector machine (SVM) parameters we use Genetic Algorithm and Support Vector Machine (GA-SVM) model. SVM is applied to construct the classifier. An intelligent classification rate of 95,6522% could be achieved using the support vector machine.
A Reinvestigation of Murine Cranial Suture Biology: Microcomputed Tomography versus Histologic Technique

Background: Histology remains the standard form to analyze cranial suture in murine models, but this technique provides only limited “snapshots” of the entire suture and requires animal euthanasia with tissue destruction. Because of the bone complex microarchitecture, better methods are required to study the behavior of the cranial suture and its surrounding environment. The authors compared microcomputed tomography and histology as techniques to evaluate murine cranial sutures. Methods: A total of 360 microcomputed tomography images and 160 to 170 histologic sections were processed from a mouse at postnatal days 22 and 45, respectively. After euthanasia, the posterior frontal and sagittal sutures were imaged with a microcomputed tomography system and subsequently processed for histologic analysis. Quantitative analysis of two-dimensional images was performed to determine the percentage of bone in a 1-mm2 sample. Results: Quantitative analysis of the percentage of bone within the sutures showed identical patterns by microcomputed tomography and histology techniques. Both methods demonstrated the posterior frontal suture to have heavier fusion patterns in the anterior and endocranial portions, with variable skip areas of complete patency on the endocranial surface, ectocranial surface, or both at day 45. Conclusions: Cranial suture fusion in the murine model is not an “all-or-none” phenomenon. The posterior frontal suture, previously thought to be completely fused on day 45 by histological analysis, showed variable fusion along the length of the suture by both methods. Quantitative assessment of the percentage of bone within the posterior frontal and sagittal sutures and morphologic assessment of these sutures demonstrated similar findings by both methods. Whereas thorough histologic evaluation of an entire suture would be extremely labor intensive and impractical, these findings help to validate microcomputed tomography as a rapid and reliable method of examining the entire suture in murine models.
Exploring venue popularity in Foursquare

In this paper, we provide a detailed analysis on the venue popularity in Foursquare, a leading location-based social network. By collecting 2.4 million venues from 14 geographic regions all over the world, we study the common characteristics of popular venues, and make the following observations. First, venues with more complete profile information are more likely to be popular. Second, venues in the Food category attract the most (43%) public tips (comments) by users, and the Travel & Transport category is the most popular category with the highest per venue check-ins, i.e., each venue in this category attracts on average 376 check-ins. Moreover, the stickiness of users checking in venues in the residence, office, and school categories is higher than in other categories. Last but not least, in general, old venues created at the early stage of Foursquare are more popular than new venues. Our results help to understand the factors that cause venues to become popular, and have applications in venue recommendations and advertisement in location based social networks.
Complete Genome Analysis of Thermus parvatiensis and Comparative Genomics of Thermus spp. Provide Insights into Genetic Variability and Evolution of Natural Competence as Strategic Survival Attributes

Thermophilic environments represent an interesting niche. Among thermophiles, the genus Thermus is among the most studied genera. In this study, we have sequenced the genome of Thermus parvatiensis strain RL, a thermophile isolated from Himalayan hot water springs (temperature >96°C) using PacBio RSII SMRT technique. The small genome (2.01 Mbp) comprises a chromosome (1.87 Mbp) and a plasmid (143 Kbp), designated in this study as pTP143. Annotation revealed a high number of repair genes, a squeezed genome but containing highly plastic plasmid with transposases, integrases, mobile elements and hypothetical proteins (44%). We performed a comparative genomic study of the group Thermus with an aim of analysing the phylogenetic relatedness as well as niche specific attributes prevalent among the group. We compared the reference genome RL with 16 Thermus genomes to assess their phylogenetic relationships based on 16S rRNA gene sequences, average nucleotide identity (ANI), conserved marker genes (31 and 400), pan genome and tetranucleotide frequency. The core genome of the analyzed genomes contained 1,177 core genes and many singleton genes were detected in individual genomes, reflecting a conserved core but adaptive pan repertoire. We demonstrated the presence of metagenomic islands (chromosome:5, plasmid:5) by recruiting raw metagenomic data (from the same niche) against the genomic replicons of T. parvatiensis. We also dissected the CRISPR loci wide all genomes and found widespread presence of this system across Thermus genomes. Additionally, we performed a comparative analysis of competence loci wide Thermus genomes and found evidence for recent horizontal acquisition of the locus and continued dispersal among members reflecting that natural competence is a beneficial survival trait among Thermus members and its acquisition depicts unending evolution in order to accomplish optimal fitness.