Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kinetic Model of Whole-Body Glucose Metabolism with Reference to the Domestic Dog (Canis lupus familiaris)

A new two-pool model to describe glucose kinetics in the steady state is presented. The pools are plasma glucose, Q 1, and tissue glucose, Q 2 (both µmol). The flows (all µmol/min) into the plasma pool (Pool 1) are absorbed glucose entry from dietary sources, labelled glucose infusion, and hepatic glucose production. There is one flow out of Pool 1, glucose uptake by the tissues. Inflows to the tissues pool (Pool 2) are from plasma and glycogenolysis. Outflows from Pool 2 are to plasma, glucose oxidation, and glycogenesis and other metabolism. Application of the model was illustrated using experimental data derived from healthy adult Labrador Retrievers in the fasted and fed (repeated meal feeding) states. In general, model derived estimates of glucose kinetics were representative of normal glucose metabolism, where rates of glucose production and uptake are similar and act to maintain blood glucose concentrations. Furthermore, estimates of within tissue glucose cycling indicated glycogenolysis in fasting and glycogenesis when fed. In the fasted state, model outputs were consistent with those reported in the canine literature derived using a single pool model.
A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems

We develop a fast and robust algorithm for solving large-scale convex composite optimization models with an emphasis on the $\ell_1$-regularized least squares regression (lasso) problems. Despite the fact that there exist a large number of solvers in the literature for the lasso problems, we found that no solver can efficiently handle difficult large-scale regression problems with real data. By leveraging on available error bound results to realize the asymptotic superlinear convergence property of the augmented Lagrangian algorithm, and by exploiting the second order sparsity of the problem through the semismooth Newton method, we are able to propose an algorithm, called Ssnal, to efficiently solve the aforementioned difficult problems. Under very mild conditions, which hold automatically for lasso problems, both the primal and the dual iteration sequences generated by Ssnal possess a fast linear convergence rate, which can even be superlinear asymptotically. Numerical comparisons between our approach an.
DGE : Influence Minimization in Networks

The diffusion of undesirable phenomena over social, information and technological networks is a common problem in different domains. Domain experts typically intervene to advise solutions that mitigate the spread in question. However, these solutions are temporary, in that they only tackle the spread of a specific instance over the network, failing to account for the complex modalities of diffusion processes. We propose an optimization formulation for the problem of minimizing the spread of influence in a network by removing some of its edges. We show that the corresponding objective function is supermodular under the linear threshold model, allowing for a greedy approximate solution with provable guarantees. Preliminary experiments on real and synthetic network data show that our method significantly outperforms other common heuristics.
Soil Respiration in a Subtropical Savanna Parkland: Response to Water Additions

Soil respiration (SR) is controlled by abiotic parameters (temperature, water availability) interacting with biotic characteristics of the vegetation (quantity and quality of litter inputs, root respiration) and the soil microbial community. Because SR is a major flux in the C cycle, it is important to understand how vegetation change may interact with predicted climate changes to alter SR and ecosystem C storage. We quantified the SR response to increased soil water availability in a subtropical savanna parkland in the Rio Grande Plains of southern Texas. Diel SR was measured monthly from July 1996 to August 1997 in control and irrigated plots located in grasslands and in three contrasting woody plant communities known to have developed on grassland during the past 100 yr. Irrigation increased SR in all plant community types. Soil respiration in woody communities was higher (12.7 g CO 2 m -2 d -1 averaged across months and treatments) and more responsive (+103%) to increased water availability than grasslands (9.8 g CO 2 m -2 d -1 and +48%, respectively). This SR pattern is probably the result of woody communities having greater soil microbial biomass, soil C pools, and root biomass than adjacent grasslands. Irrigation increased the sensitivity of SR to temperature (Q 10 = 1.6 and 2.6 for control and irrigated plots, respectively), but Q 10 values were similar in woody communities and grasslands. Results suggest SR is water limited, that sensitivity of SR to soil moisture availability increases with increasing woody plant abundance, and that shifts from grass to shrub dominance may have little impact on SR response to the changes in temperature projected to accompany global warming.
Coupled dictionary training for exemplar-based speech enhancement

In exemplar-based speech enhancement systems, lower dimensional features are preferred over the full-scale DFT features for their reduced computational complexity and the ability to better generalize for the unseen cases. But in order to obtain the Wiener-like filter for noisy DFT enhancement, the speech and noise estimates obtained in the feature space need to be mapped to the DFT space, which yield a low-rank approximation of the estimates resulting in a sub-optimal filter. This paper proposes a novel method using coupled dictionaries where the exemplars for the required feature space and the DFT space are jointly extracted and the estimates are directly obtained in the DFT space following the decomposition in the chosen feature space. Simulation experiments revealed that the proposed approach, where the activations of exemplars calculated using the Mel resolution are directly used to obtain the Wiener filter in the DFT space, results in improved signal-to-distortion ratio (SDR) when compared to the system without coupled dictionaries. To further motivate the use of coupled dictionaries, the paper also investigates the use of modulation envelope features for the exemplar-based speech enhancement.
The PCP theorem

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Tashkent: uniting durability with transaction ordering for high-performance scalable database replication

In stand-alone databases, the functions of ordering the transaction commits and making the effects of transactions durable are performed in one single action, namely the writing of the commit record to disk. For efficiency many of these writes are grouped into a single disk operation. In replicated databases in which all replicas agree on the commit order of update transactions, these two functions are typically separated. Specifically, the replication middleware determines the global commit order, while the database replicas make the transactions durable.The contribution of this paper is to demonstrate that this separation causes a significant scalability bottleneck. It forces some of the commit records to be written to disk serially, where in a standalone system they could have been grouped together in a single disk write. Two solutions are possible: (1) move durability from the database to the replication middleware, or (2) keep durability in the database and pass the global commit order from the replication middleware to the database.We implement these two solutions. Tashkent-MW is a pure middleware solution that combines durability and ordering in the middleware, and treats an unmodified database as a black box. In Tashkent-API, we modify the database API so that the middleware can specify the commit order to the database, thus, combining ordering and durability inside the database. We compare both Tashkent systems to an otherwise identical replicated system, called Base, in which ordering and durability remain separated. Under high update transaction loads both Tashkent systems greatly outperform Base in throughput and response time.
Effect of Acupuncture on Functional Connectivity of Anterior Cingulate Cortex for Bell's Palsy Patients with Different Clinical Duration

Acupuncture is widely used in the treatment of Bell's palsy (BP) in many countries, but its underlying physiological mechanism remained controversial. In order to explore the potential mechanism, changes of functional connectivity (FC) of anterior cingulate gyrus (ACC) were investigated. We collected 20 healthy (control group) participants and 28 BP patients with different clinical duration accepted resting state functional MRI (rfMRI) scans before and after acupuncture, respectively. The FC of ACC before and after acupuncture was compared with paired t-test and the detailed results are presented in the paper. Our results showed that effects of the acupuncture on FC were closely related to clinical duration in patients with BP, which suggested that brain response to acupuncture was closely connected with the status of brain functional connectivity and implied that acupuncture plays a homeostatic role in the BP treatment.
Universal Audio Steganalysis Based on Calibration and Reversed Frequency Resolution of Human Auditory System

Calibration and higher-order statistics are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non-calibrated or only based on noise removal. The goal of this study is to fill these gaps and to show that calibrated features based on re-embedding technique improve performance of audio steganalysis. Furthermore, the authors show that least significant bit is the most sensitive bit plane to data hiding algorithms, and therefore it can be employed as a universal embedding method. The proposed features also benefit from an efficient model which is tailored to the needs for audio steganalysis and represent the maximum deviation from human auditory system. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. The results show the effectiveness of the proposed method in detecting the finest traces of data hiding algorithms in very low embedding rates. The system detects Steghide at capacity of 0.06 bit per symbol with sensitivity of 98.6% (music) and 78.5% (speech). These figures are, respectively, 7.1% and 27.5% higher than the state-of-the-art results based on R-Mel-frequency cepstral coefficient features.
MemoryLane: reminiscence for older adults

Reminiscence plays an important role in the lives of older adults [8]. Many perfect the art of storytelling and enjoy its social benefits. The telling of stories of past events and experiences defines family identities and is an integral part of most cultures.Losing the ability to recollect past memories is not only disadvantageous, but can prove quite detrimental, especially to many older adults. In this paper we introduce MemoryLane, a Personal Digital Assistant (PDA) based application being developed to enhance the reminiscence capabilities of older adults. Using abilities and preferences as a basis, MemoryLane employs Artificial Intelligence (AI) techniques to adapt its multimodal interface to accommodate the needs of differing older users and to compose and recount user life-cached multimedia as memory stories.
Horn clauses and functional dependencies in complex-value databases

We extend Fagin's result on the equivalence between functional dependencies in relational databases and propositional Horn clauses. It is shown that this equivalence still holds for functional dependencies in databases that support complex values via nesting of records, lists, sets and multisets.The equivalence has several implications. Firstly, it extends a well-known result from relational databases to databases which are not in first normal form. Secondly, it characterises the implication of functional dependencies in complex-value databases in purely logical terms. The database designer can take advantage of this equivalence to reduce database design problems to simpler problems in propositional logic. An algorithm is presented for such an application. Furthermore, relational database design tools can be reused to solve problems for complex-value databases.
An Equalizer Adaptation Algorithm to Reduce Jitter in Binary Receivers

This brief describes a hardware-efficient technique for adaptive equalization that minimizes jitter in baseband binary nonreturn-to-zero serial links. Whereas traditional least mean square (LMS) adaptation minimizes the mean squared error only at the center of the received eye pattern, this algorithm explicitly includes low jitter as part of its optimization criteria. The resulting equalizer coefficients provide a compromise between maximizing noise margin and minimizing jitter at the equalizer output. The adaptation algorithm operates on 1-bit (signed) signals and can be efficiently combined with binary phase detection in an integrated receiver. Behavioral simulations for a 10-Gb/s optical fiber link show that the proposed approach improves timing margin compared with sign-sign LMS adaptation
ASTROMETRIC MICROLENSING WITH THE GAIA SATELLITE

GAIA is the 'super-Hipparcos' survey satellite selected as a Cornerstone 6 mission by the European Space Agency. GAIA can measure microlensing by the brightening of source stars. For the broad G-band photometer, the all-sky source-averaged photometric optical depth is ∼10 - 7 . There are ∼ 1300 photometric microlensing events for which GAIA will measure at least one data point on the amplified light curve. GAIA can also measure microlensing by the small excursions of the light centroid that occur during events. The all-sky source-averaged astrometric microlensing optical depth is ∼ 2.5 X 10 - 5 . Some ∼ 25 000 sources will have a significant variation of the centroid shift, together with a closest approach, during the lifetime of the mission. This is not the actual number of events that can be extracted from the GAIA data set, as the false detection rate has not been assessed. A covariance analysis is used to study the propagation of errors and the estimation of parameters from realistic sampling of the GAIA data stream of transits in the along-scan direction during microlensing events. The mass of the lens can be calculated to good accuracy if the lens is nearby so that the angular Einstein radius θ E is large; if the Einstein radius projected on to the observer plane r E is approximately an astronomical unit; or if the duration of the astrometric event is long (≥ 1 yr) or the source star is bright. Monte Carlo simulations are used to study the ∼ 2500 events for which the mass can be recovered with an error of < 50 per cent. These high-quality events are dominated by disc lenses within a few tens of parsecs and source stars within a few hundred parsecs. We show that the local mass function can be recovered from the high-quality sample to good accuracy. GAIA is the first instrument with the capability of measuring the mass locally in very faint objects such as black holes and very cool white and brown dwarfs. For only ∼ 5 per cent of all astrometric events will GAIA record even one photometric data point. There is a need for a dedicated telescope that densely samples the Galactic Centre and spiral arms, as this can improve the accuracy of parameter estimation by a factor of ∼ 10. The total number of sources that have an astrometric microlensing variation exceeding the mission target accuracy is ∼ 10 5 . The positional measurement of one source in every 20 000 is affected by microlensing noise at any instant. We show that microlensing is negligible as an unbiased random error source for GAIA.
If you are not paying for it, you are the product: How much do advertisers pay for your personal data?

Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they navigate the web. Letting the huge debate around privacy aside, a very important question in this area, for which very little is known, is: How much do advertisers pay for an individual's personal data? In this study, we develop a first of its kind methodology for computing exactly that - the price paid for one's privacy - and we do that in real time. Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads. Our main technical contribution is a method for tallying winning bids even when they are encrypted. We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns. We implement our methodology through a browser addon and a back-end server that provides it with fresh models for encrypted bids. We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's worth with more than 82% accuracy.
Adaptive TDMA/OFDMA for wide-area coverage and vehicular velocities

Within the EU FP6 Integrated Project WINNER, adaptive transmission is investigated as a key technology for boosting the spectral efficiency of a new radio interface for 4G systems. Adaptive allocation of time-frequency chunks in an OFDM-based system offers a significant potential, but also poses challenges. Within work package two of WINNER, we study critical issues such as the feasibility of adaptive transmission over fading downlink/uplink channels to/from vehicular terminals, the corresponding required channel prediction accuracy, and the required feedback control bandwidth. This paper summarizes recent results obtained within WINNER, and related results obtained within the Swedish Wireless IP project.
Fitting Parameterized 3-D Models to Images

Model-based recognition and tracking from 2-D images depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. A standardized modeling language has been developed that can be used to define models and their internal parameters for efficient application to model-based vision. These new techniques allow model- based vision to be used for a much wider class of problems than was possible with earlier methods.
A tractable genotype-phenotype map for the self-assembly of protein quaternary structure

The mapping between biological genotypes and phenotypes is central to the study of biological evolution. Here we introduce a rich, intuitive, and biologically realistic genotype-phenotype (GP) map, that serves as a model of self-assembling biological structures, such as protein complexes, and remains computationally and analytically tractable. Our GP map arises naturally from the self-assembly of polyomino structures on a 2D lattice and exhibits a number of properties: $\textit{redundancy}$ (genotypes vastly outnumber phenotypes), $\textit{phenotype bias}$ (genotypic redundancy varies greatly between phenotypes), $\textit{genotype component disconnectivity}$ (phenotypes consist of disconnected mutational networks) and $\textit{shape space covering}$ (most phenotypes can be reached in a small number of mutations). We also show that the mutational robustness of phenotypes scales very roughly logarithmically with phenotype redundancy and is positively correlated with phenotypic evolvability. Although our GP map describes the assembly of disconnected objects, it shares many properties with other popular GP maps for connected units, such as models for RNA secondary structure or the HP lattice model for protein tertiary structure. The remarkable fact that these important properties similarly emerge from such different models suggests the possibility that universal features underlie a much wider class of biologically realistic GP maps.
1-kW 2000–4500 r/min Ferrite PMSM Drive: Comprehensive Characterization and Two Sensorless Control Options

An optimal design of internal Ferrite permanent-magnet motor with embedded key finite-element method verification is presented with the scope to increase the efficiency of the small power motors without using expensive rare earth permanent magnets. Two internal flux concentration topologies are analyzed, and the geometric dimensions and performances are presented for three cases, all at 1-kW rated power at 4500 r/min rated speed. The 6/8 poles ferrite-spoke permanent-magnet synchronous motor prototype was built and test results prove an efficiency larger than 88% for a large load range. Two sensorless control methods based on active flux observer are presented, with the key simulation results including acceleration, speed reversal, loading, and unloading speed responses.
Iterative Learning Synchronization of Robotic Rehabilitation Tasks

Functional and task-oriented robotic rehabilitation training can be improved by the interaction of the patient with real-world objects. If these objects are not directly coupled to the robot, a synchronization of robot and object is required, comparable to a calibration between the end effector of an industrial robot and a work piece. Such synchronization prevents from safety hazards and unwanted tension forces at the man-machine interface between robot and patient or patient and object. In this paper, we propose a combination of an iterative learning synchronization (ILS) and an immediate error correction (IEC) technique to automate such synchronization processes. The feasibility of these methods is demonstrated by using them to automatically synchronize the gait rehabilitation robot Lokomat with a treadmilL.
Robust stability of uncertain cellular neural network for image processing - an LMI approach

In this paper, a network called a discrete-time cellular neural network (DTCNN) with uncertainty is introduced. A methodology for the robust stability of the DTCNN is presented. The uncertainties are assumed to be norm-bounded. The methodology is based on the Lyapunov functional combining with linear matrix inequality (LMI) approach. An example is provided to illustrate the effectiveness of the proposed methodology.
Comparison of NHDP and MHVB for Neighbor Discovery in Multi-hop Ad Hoc Networks

This document compares two protocols, MHVB and NHDP. While both protocols are intended for wireless multi-hop ad hoc networks, they differ fundamentally, both in operation and in purpose: MHVB is a location-based, general-purpose transport protocol for network wide information dissemination, whereas NHDP is a protocol enabling a router to acquire information describing its local network topology up to two hops away. Different as they may seem, these two protocols can, in certain situations, serve the same purpose. For example, MHVB can be employed by an ad hoc routing protocol in place of NHDP, for dissemination of topological information when location information is available. Similarly, NHDP may be used to carry certain location-based information, in place of MHVB. This document examines the viability of NHDP and MHVB for neighborhood discovery, and analyses their performance as such. Aside from the usual set of performance parameters, special interest is accorded to the "freshness" of neighborhood information, obtained through each of the protocols.