Fast PageRank approximation by adaptive sampling

In many environmental management problems, the construction of occurrence maps of species of interest is a prerequisite to their effective management. However, the construction of occurrence maps is a challenging problem because observations are often costly to obtain (thus incomplete) and noisy (thus imperfect). It is therefore critical to develop tools for designing efficient spatial sampling strategies and for addressing data uncertainty. Adaptive sampling strategies are known to be more efficient than non-adaptive strategies. Here, we develop a model-based adaptive spatial sampling method for the construction of occurrence maps. We apply the method to estimate the occurrence of one of the world's worst invasive species, the red imported fire ant, in and around the city of Brisbane, Australia. Our contribution is threefold: (i) a model of uncertainty about invasion maps using the classical image analysis probabilistic framework of Hidden Markov Random Fields (HMRF), (ii) an original exact method for optimal spatial sampling with HMRF and approximate solution algorithms for this problem, both in the static and adaptive sampling cases, (iii) an empirical evaluation of these methods on simulated problems inspired by the fire ants case study. Our analysis demonstrates that the adaptive strategy can lead to substantial improvement in occurrence mapping.

Direct volume rendering of large volumetric data sets on programmable graphics hardware is often limited by the amount of available graphics memory and the bandwidth from main memory to graphics memory. Therefore, several approaches to volume rendering from compact representations of volumetric data have been published that avoid most of the data transfer between main memory and the graphics programming unit (GPU) at the cost of additional data decompression by the GPU. To reduce this performance cost, adaptive sampling techniques were proposed; which are, however, usually restricted to the sampling in view direction. In this work, we present a GPU-based volume rendering algorithm with adaptive sampling in all three spatial directions; i.e., not only in view direction but also in the two perpendicular directions of the image plane. This approach allows us to reduce the number of samples dramatically without compromising image quality; thus, it is particularly well suited for many compressed representations of volumetric data that require a computational expensive GPU-based sampling of data.

A low power fuzzy logic based variable resolution ADC for wireless ECG monitoring systems

Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in practical applications. Strong spatial unevenness often leads to information loss in traditional geostatistical models, where critical anomaly structures may be over-smoothed or obscured. To address this limitation, this study proposes a knowledge-driven adaptive direct sampling (KD-ADS) framework. This approach functions as a geospatial context-aware reconstruction engine. It integrates a multi-factor knowledge-driven weighting system to prioritize regions with high information value and incorporates a dynamic context-aware neighborhood module that adapts to local statistical characteristics. Using 1268 samples from the Jiulian Mountains tungsten metallogenic belt, ablation studies demonstrate the individual contributions of the knowledge-driven weighting and adaptive neighborhood modules to improving reconstruction accuracy and spatial connectivity. Comparative experiments with the traditional direct sampling (DS) algorithm demonstrate that KD-ADS achieves a more accurate reconstruction of geochemical fields and better preserves discrete high-value mineralization anomalies and spatial heterogeneity under sampling-bias conditions. This approach improves the reproducibility of mineralization enrichment patterns and enhances computational efficiency, providing data science-driven support for sustainable mineral exploration and resource allocation.

Compressive sampling (CS), or Compressed Sensing , has generated a tremendous amount of excitement in the signal processing community. Compressive sampling, which involves non-traditional samples in the form of randomized projections, can capture most of the salient information in a signal with a relatively small number of samples, often far fewer samples than required using traditional sampling schemes. Adaptive sampling (AS), also called Active Learning , uses information gleaned from previous observations (e.g. , feedback) to focus the sampling process. Theoretical and experimental results have shown that adaptive sampling can dramatically outperform conventional (non-adaptive) sampling schemes. This paper compares the theoretical performance of compressive and adaptive sampling for regression in noisy conditions, and it is shown that for certain classes of piecewise constant signals and high SNR regimes both CS and AS are near optimal. This result is remarkable since it is the first evidence that shows that compressive sampling, which is non-adaptive, cannot be significantly outperformed by any other method (including adaptive sampling procedures), even in the presence of noise. The performance of CS schemes for signal detection is also investigated.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

SummaryAsian options are popular path-dependent derivatives in the financial market. However, how to price them efficiently and accurately has been a longstanding research and practical problem. No known exact pricing formulas are available to price the Asian option. Although approximate pricing formulas exist, they lack accuracy guarantees. Asian options can be priced on the lattice. A lattice divides a time interval into n equal-length time steps. It is known that the value computed by the lattice converges to the true option value as n → ∞. Unfortunately, only subexponential-time algorithms are available if Asian options are to be priced on the lattice without approximations. Efficient approximation algorithms are available for the lattice. The best known in the literature is an O(n 3.5)-time approximation lattice algorithm and an O(n 3)-time approximation PDE algorithm. Our paper suggests an O(n 2.5)-time lattice algorithm. Our algorithm uses a novel technique based on the method of Lagrange multipliers to minimize the approximation error. Numerical results verify the accuracy and the excellent performance of our algorithm.

Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system

In a Wireless Sensor Network (WSN) Nodes are distributed to monitor an area of interest such as a military battlefield, and transmit their data to in-situ Base-Station (BS) through multi-hop paths. In addition to collecting and processing the sensor data, the BS performs network management tasks. Due to such an important role, an adversary would try to locate the BS and target it with Denial-of-Service (DoS) attack in order to disrupt the WSN operation. Locating the BS can be done by intercepting transmissions and then employing traffic analysis techniques such as Evidence Theory (ET). To counter such an attack, this paper presents a novel technique for increasing BS anonymity by establishing a sleep/active schedule among the nodes that are far away from the BS, and increasing the traffic density in selected parts of the network in order to give the impression that the BS is located in the vicinity of the sleeping nodes. The simulation results confirm the effectiveness of the proposed technique in increasing the anonymity of the BS.

We present a low-cost light-weight wristband Vital as the basic element for emergency response network, which senses and reports hazardous surroundings for people who need immediate assistance such as children and seniors. Based on a multi-sensor Arduino microsystem and a low-power Bluetooth 4.1 module [1], the Vital band samples data from multiple sensors and reports to a base station, such as the guardian's cell phone or the emergency response network, for an emergency situation. With a 3D-printed case and a lithium-ion coin battery, the Vital case is weighed 13 grams and sized 35 × 30 × 11 mm3. With 10 ms short sampling pulses and adaptive sampling rates, the estimated battery lifetime can be extended to more than 100 hours for activity tracking.

A clustering-based adaptive undersampling ensemble method for highly unbalanced data classification

This paper studies some basic properties of an SEIR (Susceptible-Exposed-Infectious-Recovered) epidemic model subject to vaccination and treatment controls. Firstly, the basic stability, boundedness, and nonnegativity of the state trajectory solution are investigated. Then, the problem of partial state reachability from a certain state value to a targeted one in finite time is focused on since it turns out that epidemic models are, because of their nature, neither (state) controllable from a given state to the origin nor reachable from a given initial condition. The particular formal statement of the partial reachability is focused on as a problem of output-reachability by defining a measurable output or lower dimension than that of the state. A special case of interest is that when the output is defined as the infectious subpopulation to be step-to-step tracked under suitable amounts being compatible with the required constraints. As a result, and provided that the output-controllability Gramian is nonsingular on a certain time interval of interest, a feedback control effort might be designed so that a prescribed value of the output can be approximately tracked. A linearization approximation is performed to simplify and facilitate the above task which is based on a point-to-point linearization of the solution trajectory. To this end, an “ad hoc” sampled approximate output trajectory is defined as control objective to be targeted through a point-wise calculated Jacobian matrix. A supervised appropriate restatement of the targeted suited sampled output values is redefined, if necessary, to make the initial proposed sampled trajectory compatible with the various needed constraints on nonnegativity and control boundedness. The design can be optionally performed under constant or adaptive sampling rates. Finally, some numerical examples are given to test the theoretical aspects and the design efficiency of the model.

We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into outer and inner iterations as follows: during each outer iteration, a sample-path problem is implicitly generated using a sample of observations or “scenarios," and solved only imprecisely, to within a tolerance that is chosen adaptively, by balancing the estimated statistical error against solution error. The solutions from prior iterations serve as warm starts to aid efficient solution of the (piecewise linear convex) sample-path optimization problems generated on subsequent iterations. The generated scenarios can be independent and identically distributed, or dependent, as in Monte Carlo generation using Latin-hypercube sampling, antithetic variates, or randomized quasi-Monte Carlo. We first characterize the almost-sure convergence (and convergence in mean) of the optimality gap and the distance of the generated stochastic iterates to the true solution set. We then characterize the corresponding iteration complexity and work complexity rates as a function of the sample size schedule, demonstrating that the best achievable work complexity rate is Monte Carlo canonical and analogous to the generic $\mathcal{O}(\epsilon^{-2})$ optimal complexity for nonsmooth convex optimization. We report extensive numerical tests that indicate favorable performance, due primarily to the use of a sequential framework with an optimal sample size schedule, and the use of warm starts. The proposed algorithm can be stopped in finite time to return a solution endowed with a probabilistic guarantee on quality.

This paper devises a novel adaptive framework for the energy-aware acquisition of spectrally sparse signals. The adaptive quantized compressive sensing (CS) techniques, beyond-complementary metal–oxide–semiconductor (CMOS) hardware architecture, and corresponding algorithms which utilize them have been designed concomitantly to minimize the overall cost of signal acquisition. First, a spin-based adaptive intermittent quantizer (AIQ) is developed to facilitate the realization of the adaptive sampling technique. Next, a framework for smart and adaptive determination of the sampling rate and quantization resolution based on the instantaneous signal and hardware constraints is introduced. Finally, signal reconstruction algorithms which process the quantized CS samples are investigated. Simulation results indicate that an AIQ architecture using a spin-based quantizer incurs only 20.98- $\mu \text{W}$ power dissipation on average using 22-nm technology for 1–8 bits uniform output. Furthermore, in order to provide 8-bit quantization resolution, 85.302- $\mu \text{W}$ maximum power dissipation is attained. Our results indicate that the proposed AIQ design provides up to 6.18-mW power savings on average compared to other adaptive rate and resolution CMOS-based CS analog-to-digital converter designs. In addition, the mean square error values achieved by the simulation results confirm efficient reconstruction of the signal based on the proposed approach.

Adaptive sampling for compressed sensing based image compression

Adaptive navigation is the process by which a vehicle determines where to go based on information received while moving through the field of interest. Adaptive sampling is a specific form of this in which that information is environmental data sampled by the robot. This may be beneficial in order to save time/energy compared to a conventional navigation strategy in which the entire field is traversed. Our work in this area focuses on multi-robot gradient-based techniques for the adaptive sampling of a scalar field. To date, we have experimentally demonstrated multi-robot gradient ascent/descent as well as contour following using automated marine surface vessels. In simulation we have verified controllers for ridge descent / valley ascent as well as saddle point detection and loitering. To support rapid development of our controllers, we have developed a new testbed using wireless transmitters to establish a simple, large-scale, customizable scalar field based on the strength of the radio frequency field. A cluster of six land rovers equipped with radio signal strength sensors is then used to process sampled data, to make adaptive decisions on how to move, and to execute those moves. In this paper, we describe the technical design of the testbed, present initial experimental results, and describe our ongoing research and development work in the area of adaptive sampling and multi-robot control.