Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Estimation Algorithm for Tracking over Wireless Sensor Networks

A new distributed estimation algorithm for tracking using a wireless sensor network is presented. We investigate how to track a time varying signal, noisily sensed by the nodes of the network. The algorithm is distributed, meaning that it does not require a central coordination among the nodes. Moreover, the proposed approach is scalable with respect to the network size, which means that its complexity does not grow with respect to the total number of nodes. The algorithm designed turns out to be composed by a cascade structure. Local constraints are determined to guarantee the global asymptotic stability of the estimation error. The algorithm can be applied e.g., for the position estimation, temporal synchronization, as well as tracking of signals. Performance is illustrated by simulations, where our filter is shown to behave better than other distributed schemes proposed in the literature.
Randomized, placebo-controlled trial of pioglitazone in nondiabetic subjects with nonalcoholic steatohepatitis.

BACKGROUND & AIMS Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease for which there is limited therapy available. Insulin sensitizing, anti-inflammatory, and antifibrotic properties of thiazolidinediones support their use in treating NASH. We have evaluated pioglitazone in the treatment of nondiabetic patients with NASH. METHODS We randomized 74 nondiabetic patients (45 men; median age, 54 y) with histologically proven NASH to 12 months of standard diet, exercise, and either placebo or pioglitazone (30 mg/day). Sixty-one patients (30 placebo, 31 pioglitazone) had liver biopsies both at the beginning and the end of the study. RESULTS Compared with placebo, pioglitazone therapy was associated with an increase in weight (mean change, -0.55 vs +2.77 kg; P = .04) and a reduction in glucose (+0.4 vs -0.1 mmol/L; P = .02), HbA1c (+0.16% vs -0.18%; P = .006), insulin C peptide level (+42 vs -78 pmol/L; P = .02), alanine aminotransferase level (-10.9 vs -36.2 u/L; P = .009), gamma-glutamyltransferase level (-9.4 vs -41.2 u/L; P = .002), and ferritin (-11.3 vs -90.5 microg/L; P = .01). Histologic features including hepatocellular injury (P = .005), Mallory-Denk bodies (P = .004), and fibrosis (P = .05) were reduced in patients treated with pioglitazone compared with those in the placebo group. CONCLUSIONS Pioglitazone therapy over a 12-month period in nondiabetic subjects with NASH resulted in improvements in metabolic and histologic parameters, most notably liver injury and fibrosis. Larger extended trials are justified to assess the long-term efficacy of pioglitazone in this patient group.
Image Quality Assessment for JPEG and JPEG2000

Assessment of the compressed image quality is an important issue for image processing system. In this paper, we propose a novel objective assessment to measure the quality of gray scale compressed image, which is developed from the fundamental objective measurement. It is also correlated well with human response and least time taken comparable to some existing measurements. The proposed measurement, spatial frequency measurement (SFM) is adopted to enhance the image quality evaluation performance. From the experimental results, we found that mean average error (MAE) with SFM (MAESFM) is the suitable measurements that can be used to measure the quality of JPEG and JPEG2000 compressed images. In addition, MAESFM measurement is scaled to make them equivalent to MOS, given the rate of compressed image quality from 1 to 5 (unacceptable to excellent quality).
A Classification Taxonomy for Public Services in Iran

These days public sector provides numerous services to citizens. Identifying and managing these services is needed for establishing a national Business Reference Model (BRM). Classifying services according to their functionality provides a great view of the current state of public services and facilitates the government policymaking. This classification taxonomy can be considered as a part of the BRM. In this paper, we propose a functional classification taxonomy of the Iranian public services including government-to-government (G2G), government-to-business (G2B), and government-to-citizens (G2C) services. All of the services provided by Iranian public agencies fit into this classification. Up to now, more than two thousand of these services are classified.
Dynamic Reconfigurable Routing for High throughput in MANET

The area of adhoc networking has been receiving increasing attention among researchers in recent years, as the available wireless networking and mobile computing hardware bases are now capable of supporting the promise of this technology. Over the past few years, a variety of new routing protocols have been proposed, but the performance evaluation of each protocol shows variable results according to network parameters such as throughput, scalability, mobility and topology variation. Hence, it is challenging to find one single universal protocol or any adaptive protocol to meet all the network topology conditions, mobility patterns and scalability requirements. So, if protocols are dynamically allocated to the router nodes then it is possible to obtain an all time high throughput programmable router. Many MANET applications benefit from increased throughput, which is the focus of this work. Therefore, in this paper, a new method of dynamic reconfigurable routing (DRR) using two reactive on demand protocols viz. DSR and AODV is proposed. The DRR router monitors the throughput continuously and when the throughput plunges down below the acceptance value, it adapts to different protocol to maintain the throughput. The change of protocol to the router is done by modifying the config. in file of global mobile simulator (GLOMOSIM)
Visual Aesthetic Quality Assessment with Multi-task Deep Learning

This paper considers the problem of assessing visual aesthetic quality with semantic information. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition offers the key to addressing this problem. Based on convolutional neural networks, we propose a general multi-task framework with four different structures. In each structure, aesthetic quality assessment task and semantic recognition task are leveraged, and different features are explored to improve the quality assessment. Moreover, an effective strategy of keeping a balanced effect between the semantic task and aesthetic task is developed to optimize the parameters of our framework. The correlation analysis among the tasks validates the importance of the semantic recognition in aesthetic quality assessment. Extensive experiments verify the effectiveness of the proposed multi-task framework, and further corroborate the above proposition.
On-ground experimental verification of a torque controlled free-floating robot

In this paper, we present a comparison between two torque-based control algorithms for a free-floating robot: the first uses the generalized Jacobian transpose, while the second is based on a complete feedback linearization of the free floating robot dynamics. The analyzed task is to follow a desired trajectory (e.g. provided by a motion planner) and the controllers must satisfy predefined compliance and impedance conditions for the manipulator end effector. The effectiveness of the algorithms and the performance are demonstrated in simulation and through experiments on a hardware-in-the-loop facility.
Graph-regularized dual Lasso for robust eQTL mapping

Motivation: As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated. Results: To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods. Availability: Software for both C++ version and Matlab version is available at http://www.cs. unc.edu/∼weicheng/. Contact: weiwang@cs.ucla. edu Supplementary information: Supplementary data are available at Bioinformatics online.
Improving interactive performance using TIPME

On the vast majority of today's computers, the dominant form of computation is GUI-based user interaction. In such an environment, the user's perception is the final arbiter of performance. Human-factors research shows that a user's perception of performance is affected by unexpectedly long delays. However, most performance-tuning techniques currently rely on throughput-sensitive benchmarks. While these techniques improve the average performance of the system, they do little to detect or eliminate response-time variabilities—in particular, unexpectedly long delays. We introduce a measurement infrastructure that allows us to improve user-perceived performance by helping us to identify and eliminate the causes of the unexpected long response times that users find unacceptable. We describe TIPME (The Interactive Performance Monitoring Environment), a collection of measurement tools that allowed us to quickly and easily diagnose interactive performance “bugs” in a mature operating system. We present two case studies that demonstrate the effectiveness of our measurement infrastructure. Each of the performance problems we identify drastically affects variability in response time in a mature system, demonstrating that current tuning techniques do not address this class of performance problems.
Minimum risk methods in the estimation of unknown sparsity

The selection of significant components in a sparse vector, d irectly observed with i.i. d. observational noise, typical ly proceeds by thresholding the observations. The objective in this paper is to choose the threshold that minimizes the ris k (expected squared prediction error) of the estimator with r espect to the noise-free sparse vector. The risk as a functio n of the model size (or, equivalently, as a function of the thre shold value) is estimated by Stein’s Unbiased Risk Estimato r (SURE) and by Generalized Cross Validation (GCV). Minimization of these criteria leads to additional randomness, generally not taken into account. A correction for this minimization randomness is proposed. The analysis starts from the observation that soft-thresholding data with normal noise allows unbiased risk estimators. The step towards hardthresholding is then generalized to other noise models and — through a link between SURE and Mallows’ Cp — to other selection criteria. The case of Akaike’s Information Crite rion (AIC) is further elaborated. In a second theme of this paper, the use of GCV for sparse model selection is motivated by two new theorems, one for the asymptotic behavior near the optimal threshold, the other for the asymptotic behavior for thresholds close to zero. A third theme of this paper is the application of GCV to iterative threshold procedures for estimating sparse vectors from indirect observations, such as in inverse problems, compressed sensing and so on. We propose to iterate with a step-dependent threshold and find experimentally that this allows closer fit than any fixed thre shold.
Homogeneity and change-point detection tests for multivariate data using rank statistics

Detecting and locating changes in highly multivariate data is a major concern in several current statistical applications. In this context, the first contribution of the paper is a novel non-parametric two-sample homogeneity test for multivariate data based on the well-known Wilcoxon rank statistic. The proposed two-sample homogeneity test statistic can be extended to deal with ordinal or censored data as well as to test for the homogeneity of more than two samples. We also provide a detailed analysis of the power of the proposed test statistic (in the two sample case) against asymptotic local shift alternatives. The second contribution of the paper concerns the use of the proposed test statistic to perform retrospective change-point detection. It is first shown that the approach is computationally feasible even when looking for a large number of change-points thanks to the use of dynamic programming. Computable asymptotic p-values for the test are available in the case where a single potential change-point is to be detected. The proposed approach is particularly recommendable in situations where the correlations between the coordinates of the data are moderate, the marginal distributions are not well modelled by usual parametric assumptions (e.g., in the presence of outliers) and when faced with highly variable change patterns, for instance, if the potential changes only affect subsets of the coordinates of the data.
Hydrogen production from inexhaustible supplies of fresh and salt water using microbial reverse-electrodialysis electrolysis cells

There is a tremendous source of entropic energy available from the salinity difference between river water and seawater, but this energy has yet to be efficiently captured and stored. Here we demonstrate that H2 can be produced in a single process by capturing the salinity driven energy along with organic matter degradation using exoelectrogenic bacteria. Only five pairs of seawater and river water cells were sandwiched between an anode, containing exoelectrogenic bacteria, and a cathode, forming a microbial reverse-electrodialysis electrolysis cell. Exoelectrogens added an electrical potential from acetate oxidation and reduced the anode overpotential, while the reverse electrodialysis stack contributed 0.5–0.6 V at a salinity ratio (seawater:river water) of 50. The H2 production rate increased from 0.8 to 1.6 m3-H2/m3-anolyte/day for seawater and river water flow rates ranging from 0.1 to 0.8 mL/ min. H2 recovery, the ratio of electrons used for H2 evolution to electrons released by substrate oxidation, ranged from 72% to 86%. Energy efficiencies, calculated from changes in salinities and the loss of organic matter, were 58% to 64%. By using a relatively small reverse electrodialysis stack (11 membranes), only ∼1% of the produced energy was needed for pumping water. Although Pt was used on the cathode in these tests, additional tests with a nonprecious metal catalyst (MoS2) demonstrated H2 production at a rate of 0.8 m3/m3/d and an energy efficiency of 51%. These results show that pure H2 gas can efficiently be produced from virtually limitless supplies of seawater and river water, and biodegradable organic matter.
Robust design of cognitive radio networks

We develop a new framework for designing robust cognitive radio networks using white channels. Our framework assigns white channels to wireless links and provisions spare channel capacity that will used in the event of channel preemption. We characterize the optimal white channel assignment that minimizes the spare capacity while guaranteeing survivability against white channel preemptions.
An Integrated ISFETs Instrumentation System in Standard CMOS Technology

This paper describes an integrated ISFETs instrumentation system in a 0.18 μm 1-poly-6-metal CMOS process. The chip is able to compute the average of CMOS ISFETs' threshold voltages by using an averaging array employing global negative current feedback. In addition, neither reference voltage nor current is required to set up the sigma-delta modulator because the internal signal is converted and processed in the frequency domain. The chip operates at 3.3 V for the analog blocks and the digital input/output blocks, and at 1.8 V for the core digital logic. It achieves 8 bits accuracy under 80 μW static power consumption. The die area is 2.6 mm2.
The Multics kernel design project

We describe a plan to create an auditable version of Multics. The engineering experiments of that plan are now complete. Type extension as a design discipline has been demonstrated feasible, even for the internal workings of an operating system, where many subtle intermodule dependencies were discovered and controlled. Insight was gained into several tradeoffs between kernel complexity and user semantics. The performance and size effects of this work are encouraging. We conclude that verifiable operating system kernels may someday be feasible.
A mobility prediction system leveraging realtime location data streams: poster

Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to train the prediction models in a centralised server. This amounts to considerably long waiting times before the model kicks in. Disclosing highly sensitive location information to third party entities also exposes the user to several privacy risks. To address these issues, we put forth a mobility prediction system, able to provide swift realtime predictions, evading the strenuous training procedure. We enable this by constantly adapting the model to substantive user mobility behaviours that facilitate accurate predictions even on marginal time bounded movements. In comparison to existing frameworks, we utilise less volumes of data to produce satisfactory prediction accuracies. This in turn lowers the computational complexity making implementation on mobile devices feasible and a step towards privacy preservation. Here, only the predicted location can be sent to such services to maintain the utility/privacy tradeoff. Our preliminary evaluations based on real world mobility traces corroborate our hypothesis.
Compressing hexahedral volume meshes

Unstructured hexahedral volume meshes are of particular interest for visualization and simulation applications. They allow regular tiling of the three-dimensional space and show good numerical behaviour in finite element computations. Beside such appealing properties, volume meshes take huge amount of space when stored in a raw format. We present a technique for encoding connectivity and geometry of unstructured hexahedral volume meshes. For connectivity compression, we extend the idea of coding with degrees as pioneered by Touma and Gotsman (1998) to volume meshes. Hexahedral connectivity is coded as a sequence of edge degrees. This naturally exploits the regularity of typical hexahedral meshes. We achieve compression rates of around 1.5 bits per hexahedron (bph) that go down to 0.18 bph for regular meshes. On our test meshes the average connectivity compression ratio is 1:162.7. For geometry compression, we perform simple parallelogram prediction on uniformly quantized vertices within the side of a hexahedron. Tests show an average geometry compression ratio of 1:3.7 at a quantization level of 16 bits.
An enterprise search paradigm based on extended query auto-completion: do we still need search and navigation?

Enterprise query auto-completion (QAC) can allow website or intranet visitors to satisfy a need more efficiently than traditional searching and browsing. The limited scope of an enterprise makes it possible to satisfy a high proportion of information needs through completion. Further, the availability of structured sources of completions such as product catalogues compensates for sparsity of log data. Extended forms (X-QAC) can give access to information that is inaccessible via a conventional crawled index. We show that it can be guaranteed that for every suggestion there is a prefix which causes it to appear in the top k suggestions. Using university query logs and structured lists, we quantify the significant keystroke savings attributable to this guarantee (worst case). Such savings may be of particular value for mobile devices. A user experiment showed that a staff lookup task took an average of 61% longer with a conventional search interface than with an X-QAC system. Using wine catalogue data we demonstrate a further extension which allows a user to home in on desired items in faceted-navigation style. We also note that advertisements can be triggered from QAC. Given the advantages and power of X-QAC systems, we envisage that websites and intranets of the [near] future will provide less navigation and rely less on conventional search.
Toward multistandard mobile terminals - fully integrated receivers requirements and architectures

In the recent past, there has been an evolution in wireless communications toward multifunctions and multistandard mobile terminals. Reducing the number of external components to a minimum is key when the same mobile terminal has to process several different standards. Highly integrated solutions in low-cost silicon technologies are thus required. Zero-IF and low-IF receiver architectures are most suitable for a high level of integration. This paper presents a review of global system for mobile communications, universal mobile telecommunication system, Bluetooth, and wireless local area network (IEEE802.11a, b, g and HiperLAN2) standards, likely to all be present in the "universal" terminal of the future, enabling global roaming and wireless connectivity. The various standards are analyzed in order to find the optimal architecture and the building-block specifications for the receive section, with particular care to the RF front-end. State-of-the-art solutions are discussed, with emphasis on direct conversion CMOS implementations. A multistandard architecture for a fully-integrated CMOS receiver is proposed.
SecondSkin

SecondSkin is a sketch-based modeling system focused on the creation of structures comprised of layered, shape interdependent 3D volumes. Our approach is built on three novel insights gleaned from an analysis of representative artist sketches. First, we observe that a closed loop of strokes typically define surface patches that bound volumes in conjunction with underlying surfaces. Second, a significant majority of these strokes map to a small set of curve-types, that describe the 3D geometric relationship between the stroke and underlying layer geometry. Third, we find that a few simple geometric features allow us to consistently classify 2D strokes to our proposed set of 3D curve-types. Our algorithm thus processes strokes as they are drawn, identifies their curve-type, and interprets them as 3D curves on and around underlying 3D geometry, using other connected 3D curves for context. Curve loops are automatically surfaced and turned into volumes bound to the underlying layer, creating additional curves and surfaces as necessary. Stroke classification by 15 viewers on a suite of ground truth sketches validates our curve-types and classification algorithm. We evaluate SecondSkin via a compelling gallery of layered 3D models that would be tedious to produce using current sketch modelers.