Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed Double-Precision Computation of Reciprocal, Division, Square Root and Inverse Square Root

A new method for the high-speed computation of double-precision floating-point reciprocal, division, square root, and inverse square root operations is presented in this paper. This method employs a second-degree minimax polynomial approximation to obtain an accurate initial estimate of the reciprocal and the inverse square root values, and then performs a modified Goldschmidt iteration. The high accuracy of the initial approximation allows us to obtain double-precision results by computing a single Goldschmidt iteration, significantly reducing the latency of the algorithm. Two unfolded architectures are proposed: the first one computing only reciprocal and division operations, and the second one also including the computation of square root and inverse square root. The execution times and area costs for both architectures are estimated, and a comparison with other multiplicative-based methods is presented. The results of this comparison show the achievement of a lower latency than these methods, with similar hardware requirements.
Head pose estimation via probabilistic high-dimensional regression

This paper addresses the problem of head pose estimation with three degrees of freedom (pitch, yaw, roll) from a single image. Pose estimation is formulated as a high-dimensional to low-dimensional mixture of linear regression problem. We propose a method that maps HOG-based descriptors, extracted from face bounding boxes, to corresponding head poses. To account for errors in the observed bounding-box position, we learn regression parameters such that a HOG descriptor is mapped onto the union of a head pose and an offset, such that the latter optimally shifts the bounding box towards the actual position of the face in the image. The performance of the proposed method is assessed on publicly available datasets. The experiments that we carried out show that a relatively small number of locally-linear regression functions is sufficient to deal with the non-linear mapping problem at hand. Comparisons with state-of-the-art methods show that our method outperforms several other techniques.
Dual Active-Capacitive-Feedback Compensation for Area-Efficient Three-Stage Amplifiers

This paper presents a dual active-capacitive-feedback compensation (DACFC) scheme for a three-stage amplifier in large capacitive-load applications. The proposed DACFC is first reported scheme, which can generate two left-half-plane zeros to compensate for the negative phase shift due to the amplifier non-dominant complex poles. The value of compensation capacitors can thus be significantly reduced to widen the gain-bandwidth product and decrease the chip area while maintaining the stability of the amplifier. Implemented in a standard 0.35mum CMOS process, the proposed amplifier only requires the total compensation capacitance of 4.3pF to achieve the gain-bandwidth product of 5.2MHz and the phase margin of 61deg when driving a 500pF//25kOmega load. The amplifier dissipates 330muW power under a 2V input supply.
Resource competition and community structure.

One of the central questions of ecology is why there are so many different kinds of plants and animals. Here David Tilman presents a theory of how organisms compete for resources and the way their competition promotes diversity. Developing Hutchinson's suggestion that the main cause of diversity is the feeding relations of species, this book builds a mechanistic, resource-based explanation of the structure and functioning of ecological communities. In a detailed analysis of the Park Grass Experiments at the Rothamsted Experimental Station in England, the author demonstrates that the dramatic results of these 120 years of experimentation are consistent with his theory, as are observations in many other natural communities. The consumer-resource approach of this book is applicable to both animal and plant communities, but the majority of Professor Tilman's discussion concentrates on the structure of plant communities. All theoretical arguments are developed graphically, and formal mathematics is kept to a minimum. The final chapters of the book provide some testable speculations about resources and animal communities and explore such problems as the evolution of "super species," the differences between plant and animal community diversity patterns, and the cause of plant succession.
Recovering human pose in 3D by visual manifolds

We describe a mechanism based upon activity manifolds that map image data from more than one view to spatial pose. We learn the manifolds from training data which are motion capture data about real human subjects exercising the target actions. The nature of the training data allows the learned manifolds to conform naturally to multiple constraints, including (1) the body-part articulation constraint; (2) the image-consistency constraint; and (3) conformation to prior information about the possible human activities. A mirror function is specifically designed that allows the system to pick up the proper manifold in multi-activity scenario. Human pose in both the image space and 3D is expressed in terms of the body-part joint positions. Such a representation allows image data to be related across views and to 3D space with ease. Experimental results show that not only do the manifolds effectively map image data to 3D pose; the presence of multiple images also improves the precision of the recovered pose and helps fix feature extraction error in any single image.
Training Convolutional Neural Nets

T = {(x1,y1), . , (xN ,yN )} . The outputs yn are scalars in a classification problem or possibly vectors, either for regression or when the deep net is used to generate a feature vector. The supervised training of this net starts by setting the entries in the weight matrices W `, which are collectively called the parameters of the net, to fit1 the input-output pairs in T . Fitting in turn involves minimizing the training error
Is Sprawl Unhealthy?

This article addresses the contention that urban sprawl influences general health through physical activity, obesity, and the presence of chronic disease. Data on individual health is obtained from the National Health and Nutrition Examination Survey III study in 29 primary metropolitan statistical areas, and data on sprawl are from Ewing et al. Using hierarchical modeling, the results indicate that even with strong controls for individual variables, residents of areas with more highly accessible and gridded street networks have higher health ratings. At the same time, residents of more densely populated urban areas have lower rated health, net of individual-level measures. Measures of sprawl have no significant relationship to frequency of walking, body mass index, or diagnosis of various chronic diseases. However, among those with chronic conditions, including hypertension, diabetes, and lung disease, those who live in areas with more highly connected street networks have higher rated health.
Cooperative distributed MIMO channels in wireless sensor networks

The large number of network nodes and the energy constraints make Wireless Sensor Networks (WSN) one of the most important application fields for Cooperative Diversity. Node cooperation increases the spatial diversity of wireless channels and, thus, reduces the transmitted power. In this paper, we propose a multi-hop WSN with nodes grouped in cooperative clusters that exploits transmit and receive cooperation among cluster nodes. Multi-hop transmission is carried out by concatenating single cluster-to-cluster hops, where every cluster-to-cluster link is defined as a cooperative distributed multiple-input-multiple-output (MIMO) channel. Transmit diversity is exploited through a time-division, decoder-and-forward, relaying scheme based upon two time slots: the Intracluster Slot, used for data sharing within the cluster, and the Intercluster Slot, used for transmission between clusters. At the receiver side, a distributed reception protocol is devised based upon a Selection Diversity algorithm. The proposed multi-hop cooperative WSN is optimally designed for minimum end-to-end outage probability by deriving the optimum time and power allocated on the intracluster and intercluster slots of every single hop, given a per-link energy constraint. A simplified suboptimum resource allocation is also proposed, which performs close to the optimal policy. Results show that the proposed scheme achieves diversity equal to the equivalent MIMO system and significantly reduces energy consumption with respect to. the non-cooperative channel
Physiological Response of Sweet Wormwood to Salt Stress under Salicylic Acid Application and Non Application Conditions

Soil salinity is one of the major environmental stresses affecting plant growth and productivity. To assess the effect of salt stress on physiological traits of sweet wormwood medicinal plant (Artemisia annua L.) under salicylic acid application and non application conditions a field study was conducted in Zanjan, Iran during 20102011 crop year in a fourreplicatedfactorial design laid out in randomized complete block with four salinity levels (0 (control), 4, 8, and 12 ds. m NaCl) and two salicylic acid levels (salicylic acid non application (control) and salicylic acid application (0.5 mM solution). Chlorophyll a content, chlorophyll b content, leaf electrical conductivity, stomatal resistance, leaf relative water content, and canopy temperature difference were determined. Results revealed that application of salicylic acid in both stress and non stress conditions increased the chlorophyll a and b content and also leaf relative water content, although leaf electrical conductivity, stomatal resistance and canopy temperature difference decreased by salicylic acid application in both stress and non stress conditions. [Kourosh Eskandari Zanjani, Amir Hossein Shirani Rad, Zahra Bitarafan, Amin Moradi Aghdam, Tofigh Taherkhani, Pezhman Khalili. Physiological Response of Sweet Wormwood to Salt Stress under Salicylic Acid Application and Non Application Conditions. Life Sci J 2012;9(4):4190-4195]. (ISSN: 1097-8135). http://www.lifesciencesite. com. 625
Persistent Legionella Infection in a Patient after Bone Marrow Transplantation

ABSTRACT We report on a patient who developed Legionellapneumonia after bone marrow transplantation. Despite appropriate antibiotic treatment, disease progressed. The patient developed a lung abscess from which Legionella andPrevotella were isolated. Cure was achieved by surgical resection. The resected material was sterile, but 16S ribosomal DNA analysis revealed Legionella DNA.
Screening Estrogenic Activities of Chemicals or Mixtures In Vivo Using Transgenic (cyp19a1b-GFP) Zebrafish Embryos

The tg(cyp19a1b-GFP) transgenic zebrafish expresses GFP (green fluorescent protein) under the control of the cyp19a1b gene, encoding brain aromatase. This gene has two major characteristics: (i) it is only expressed in radial glial progenitors in the brain of fish and (ii) it is exquisitely sensitive to estrogens. Based on these properties, we demonstrate that natural or synthetic hormones (alone or in binary mixture), including androgens or progestagens, and industrial chemicals induce a concentration-dependent GFP expression in radial glial progenitors. As GFP expression can be quantified by in vivo imaging, this model presents a very powerful tool to screen and characterize compounds potentially acting as estrogen mimics either directly or after metabolization by the zebrafish embryo. This study also shows that radial glial cells that act as stem cells are direct targets for a large panel of endocrine disruptors, calling for more attention regarding the impact of environmental estrogens and/or certain pharmaceuticals on brain development. Altogether these data identify this in vivo bioassay as an interesting alternative to detect estrogen mimics in hazard and risk assessment perspective.
Non-asymptotic approach to varying coefficient model

In the present paper we consider the varying coefficient model which represents a useful tool for exploring dynamic patterns in many applications. Existing methods typically provide asymptotic evaluation of precision of estimation procedures under the assumption that the number of observations tends to infinity. In practical applications, however, only a finite number of measurements are available. In the present paper we focus on a non-asymptotic approach to the problem. We propose a novel estimation procedure which is based on recent developments in matrix estimation. In particular, for our estimator, we obtain upper bounds for the mean squared and the pointwise estimation errors. The obtained oracle inequalities are non-asymptotic and hold for finite sample size. AMS 2000 subject classifications: Primary 62J99, 62H12; secondary 60G57.
Time-varying stabilization of a class of driftless systems satisfying second-order controllability conditions

In this paper, we propose a stabilization scheme for nonlinear control systems whose vector fields satisfy Hormander's condition with the second-order Lie brackets. This scheme is based on the use of trigonometric controls with bounded frequencies. By using the Volterra series and a modification of Lyapunov's direct method, we reduce the stabilization problem to a system of cubic equations and prove its local solvability. Our approach ensures exponential stability of the equilibrium and gives explicit formulas for the coefficients of the control functions. The proposed methodology is illustrated by a rolling disc example.
The Impacts of Building Orientation on Polarimetric Orientation Angle Estimation and Model-Based Decomposition for Multilook Polarimetric SAR Data in Urban Areas

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables

A formidable challenge in uncertainty modeling in general, and when learning Bayesian networks in particular, is the discovery of unknown hidden variables. Few works that tackle this task are typically limited to discrete or Gaussian domains, or to tree structures. We propose a novel approach for discovering hidden variables in flexible nonGaussian domains using the powerful class of Gaussian copula networks. Briefly, we define the concept of a hypothetically optimal predictor of variable, and show how it can be used to discover useful hidden variables in the expressive framework of copula networks. We demonstrate the merit of our approach for learning succinct models that generalize well in several real-life domains.
Homogeneous finite-time consensus tracking of high-order-integrator agents by parametric approach

ABSTRACT Finite-time consensus tracking of high-order-integrator multi-agent systems (MAS) is investigated under an undirected topology. When the leader's control input is known to all followers, the homogeneous finite-time control for a high-order integrator is extended to a distributed protocol for the corresponding MAS, and a set of control gains are found by the parametric approach for robust control, such that the multiple agents are simultaneously stabilised in finite time by keeping all characteristic polynomials Hurwitz. When it is only known to the leader's neighbouring followers, a distributed observer is presented for each follower to estimate it in finite time, and the combined observer-based protocol achieves finite-time consensus tracking in a fully distributed fashion. Simulation examples illustrate the effectiveness of the proposed scheme.
Coded Network Function Virtualization: Fault Tolerance via In-Network Coding

Network function virtualization (NFV) prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. In contrast, this letter proposes to leverage channel coding in order to enhance the robustness on NFV to hardware failure. The proposed approach targets the network function of uplink channel decoding, and builds on the algebraic structure of the encoded data frames in order to perform in-network coding on the signals to be processed at different servers. The key principles underlying the proposed coded NFV approach are presented for a simple embodiment and extensions are discussed. Numerical results demonstrate the potential gains obtained with the proposed scheme as compared to the conventional diversity-based fault-tolerant scheme in terms of error probability.
Mencius: A Chinese Named Entity Recognizer Using Hybrid Model

This paper presents a maximum entropy based Chinese named entity recognizer (NER): Mencius. It aims to address Chinese NER problems by combining the advantages of rule-based and machine learning (ML) based NER systems. Rule-based NER systems can explicitly encode human comprehension and can be tuned conveniently, while ML-based systems are robust, portable and inexpensive to develop. Our hybrid system incorporates a rule-based knowledge representation and template-matching tool, InfoMap [1], into a maximum entropy (ME) framework. Named entities are represented in InfoMap as templates, which serve as ME features in Mencius. These features are edited manually and their weights are estimated by the ME framework according to the training data. To avoid the errors caused by word segmentation, we model the NER problem as a character-based tagging problem. In our experiments, Mencius outperforms both pure rule-based and pure ME-based NER systems. The F-Measures of person names (PER), location names (LOC) and organization names (ORG) in the experiment are respectively 92.4%, 73.7% and 75.3%.
Least-Cost Opportunistic Routing

In opportunistic routing, each node maintains a group of candidate relays to reach a particular destination, and transmits packets to any node in this group. If a single candidate relay receives the packet, it becomes the effective relay to forward the packet further. If no candidate receives the packet, then the current sender re-transmits. If multiple candidates receive the packet, then the link layer chooses a single receiver to be the relay. This choice could be made at random, or it could be driven by information coming from the routing layer, for example to use the best receiver as the relay. This paper addresses the least-cost opportunistic routing (LCOR) problem: how to assign and prioritize the set of candidate relays at each node for a given destination such that the expected cost of forwarding a packet to the destination is minimized. We solve this problem with a distributed algorithm that provably computes the optimal assignment of candidate relays that each node should allow to reach a particular destination. Prior proposals based on single-path routing metrics or geographic coordinates do not explicitly consider this tradeoff, and as a result make choices which are not always optimal.
Low-power high-voltage high-frequency power supply for ozone generation

A low-cost high-frequency power supply for ozone generation is presented in this paper. The paper addresses two important issues. Firstly, the ozone reactor is tested at high frequency and a new model of this type of load is proposed. Secondly, the power supply stage is presented and analyzed, obtaining some important characteristics that allow the correct design of this power converter. Simulation and experimental results are also provided to evaluate the possibilities of the proposed converter.
Task-based visual saliency for intelligent compression

In this paper we develop a new method for highlighting visually salient regions of an image based upon a known visual search task. The proposed method uses a robust model of instantaneous visual attention (i.e. “bottom-up”) combined with a pixel probability map derived from the automatic detection of a previously-seen object (task-dependent i.e. “top-down”). The objects to be recognised are parameterised quickly in advance by a viewpoint-invariant spatial distribution of SURF interest-points. The bottom-up and top-down object probability images are fused to produce a task-dependent saliency map. We validate our method using observer eye-tracker data collected under object search-and-count tasking. Our method shows 10% higher overlap with true attention areas under task compared to bottom-up saliency alone. The new combined saliency map is further used to develop a new intelligent compression technique which is an extension of DCT encoding. We demonstrate our technique on surveillance-style footage throughout.
NICMOS Observations of Interaction-triggered Star Formation in the Luminous Infrared Galaxy NGC 6090

High-resolution 1.1, 1.6, and 2.2 μm imaging of the luminous infrared galaxy NGC 6090 obtained with NICMOS on the Hubble Space Telescope is presented. These new observations are centered on the two nuclei of the merger and reveal the spiral structure of the eastern galaxy and the amorphous nature of the western galaxy. The nuclear separation of 3.2 kpc (H0 = 75 km s-1 Mpc-1) indicates that NGC 6090 is at an intermediate stage of merging. Bright knots/clusters are also visible in the region overlapping the merging galaxies; four of these knots appear bluer than the underlying galaxies and have colors consistent with young (≤107 yr) star clusters. The spatial coincidence of the knots with the molecular gas in NGC 6090 indicates that much of the present star formation is occurring outside the nuclear region of merging galaxies, consistent with recent studies of other double nucleus luminous infrared galaxies.