Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Screening for Artificial Selection during Domestication and Improvement in Maize

Background Artificial selection results in phenotypic evolution. Maize (Zea mays L. ssp. mays) was domesticated from its wild progenitor teosinte (Zea mays subspecies parviglumis) through a single domestication event in southern Mexico between 6000 and 9000 years ago. This domestication event resulted in the original maize landrace varieties. The landraces provided the genetic material for modern plant breeders to select improved varieties and inbred lines by enhancing traits controlling agricultural productivity and performance. Artificial selection during domestication and crop improvement involved selection of specific alleles at genes controlling key morphological and agronomic traits, resulting in reduced genetic diversity relative to unselected genes. Scope This review is a summary of research on the identification and characterization by population genetics approaches of genes affected by artificial selection in maize. Conclusions Analysis of DNA sequence diversity at a large number of genes in a sample of teosintes and maize inbred lines indicated that approx. 2 % of maize genes exhibit evidence of artificial selection. The remaining genes give evidence of a population bottleneck associated with domestication and crop improvement. In a second study to efficiently identify selected genes, the genes with zero sequence diversity in maize inbreds were chosen as potential targets of selection and sequenced in diverse maize landraces and teosintes, resulting in about half of candidate genes exhibiting evidence for artificial selection. Extended gene sequencing demonstrated a low false-positive rate in the approach. The selected genes have functions consistent with agronomic selection for plant growth, nutritional quality and maturity. Large-scale screening for artificial selection allows identification of genes of potential agronomic importance even when gene function and the phenotype of interest are unknown. These approaches should also be applicable to other domesticated species if specific demographic conditions during domestication exist.
Spatialized Transfer Functions

Multi-dimensional transfer functions are an efficient way to visualize features in scalar volume data produced by CT or MRI scanners. However, the optimal transfer function is difficult to find in general. We present an automatic yet powerful method for the automatic setup of multi-dimensional transfer functions by adding spatial information to the histogram of a volume. Using this information we can easily classify the histogram and derive a transfer function by assigning unique colors to each class of the histogram. Each feature can be selected interactively by pointing and clicking at the corresponding class in the transfer function. In order to render the classified volume with adequate quality we propose an extension of the wellknown pre-integration technique. Furthermore, we demonstrate the flexibility of our approach by giving examples for the imaging of segmented, diffusion-tensor and multi-modal data.
Toward Clustering Persian Vowel Viseme: A New Clustering Approach based on HMM

This paper sorts out the problem of Persian Vowel viseme clustering. Clustering audio-visual data has been discussed for a decade or so. However, it is an open problem due to shortcoming of appropriate data and its dependency to target language. Here, we propose a speaker-independent and robust method for Persian viseme class identification as our main contribution. The overall process of the proposed method consists of three main steps including (I) Mouth region segmentation, (II) Feature extraction, and (IV) Hierarchical clustering. After segmenting the mouth region in all frames, the feature vectors are extracted based on a new look at Hidden Markov Model. This is another contribution to this work, which utilizes HMM as a probabilistic model-based feature detector. Finally, a hierarchical clustering approach is utilized to cluster Persian Vowel viseme. The main advantage of this work over others is producing a single clustering output for all subjects, which can simplify the research process in other applications. In order to prove the efficiency of the proposed method a set of experiments is conducted on AVAII.
Achievable performance gain of IEEE 802.11 multi-rate link adaptation algorithm with cross-layer design

Link quality-based rate adaptation has been widely used for IEEE 802.11 networks. However, network performance is affected by both link quality and random channel access. Selection of transmit modes for optimal link throughput can cause medium access control MAC throughput loss. In this paper, we investigate this issue and propose a generalised cross-layer rate adaptation algorithm. It considers jointly link quality and channel access to optimise network throughput. The objective is to examine the potential benefits by cross-layer design. An efficient analytic model is proposed to evaluate rate adaptation algorithms under dynamic channel and multi-user access environments. The proposed algorithm is compared to link throughput optimisation-based algorithm. It is found rate adaptation by optimising link layer throughput can result in large performance loss, which cannot be compensated by the means of optimising MAC access mechanism alone. Results show cross-layer design can achieve consistent and considerable performance gains of up to 20%. It deserves to be exploited in practical design for IEEE 802.11 networks.
Outphasing transmitters, enabling digital-like amplifier operation with high efficiency and spectral purity

An overview of outphasing transmitters is given. Starting from its basic principles, we discuss its advantages and drawbacks compared to other high-efficiency amplifier configurations. Next, innovations in outphasing architecture, design, and implementation are given that overcome the difficulties related to the traditional outphasing configuration. Using the latest insights in drive schemes for input signals and quasi load-insensitive class-E output matching, very compact high-power high-performance digital- like transmitters are enabled that offer high efficiency and high spectral purity while being reconfigurable in their operating frequency.
Improving Cloud Service Resilience Using Brownout-Aware Load-Balancing

We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a load-balancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading user experience (e.g., disabling recommendations). Combining resilience with the brownout paradigm is to date an open practical problem. The issue is to ensure that replica self-adaptivity would not confuse the load-balancing algorithm, overloading replicas that are already struggling with capacity shortage. For example, load-balancing strategies based on response times are not able to decide which replicas should be selected, since the response times are already controlled by the brownout paradigm. In this paper we propose two novel brownout-aware load-balancing algorithms. To test their practical applicability, we extended the popular lighttpd web server and load-balancer, thus obtaining a production-ready implementation. Experimental evaluation shows that the approach enables cloud services to remain responsive despite cascading failures. Moreover, when compared to Shortest Queue First (SQF), believed to be near-optimal in the non-adaptive case, our algorithms improve user experience by 5%, with high statistical significance, while preserving response time predictability.
Engaging consumers in medicaid nursing home reimbursement policy: lessons from New York and Minnesota.

PURPOSE OF THE STUDY This study draws lessons for successful consumer engagement in Medicaid nursing home (NH) reimbursement policy from New York and Minnesota. In these two states, resident advocates have influenced reimbursement policy to better encourage access, care quality, and quality of life. DESIGN AND METHODS Twenty-four semi-structured interviews were conducted, including consumer advocates, state agency officials, legislators/legislative staff, and NH industry representatives. Transcripts were coded to identify recurring themes and patterns in responses. Related documents were reviewed as well. FINDINGS Interviewees report that consumer advocates should participate in NH reimbursement policy making to provide a unique perspective distinct from other stakeholder groups. Skills necessary for successful participation include developing, demonstrating, and sharing expertise to gain credibility as a legitimate actor. Effective strategies include participating on state workgroups and taskforces and developing and accessing relationships with key legislative and executive branch officials. A division of labor may be useful with those consumer advocates possessing expertise in NH reimbursement policy partnering with other organizations with the experience and resources necessary to marshal grassroots support. IMPLICATIONS State and federal reimbursement reform provide opportunities for consumer advocacy influence during the design and implementation process. The experience of consumer groups in New York and Minnesota provide lessons for advocates looking to influence these and other reimbursement initiatives.
The Grand Challenges and Myths of Neural-Symbolic Computation

The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing re- search issue in the field. The combination of logic-based inference and connec- tionist learning systems may lead to the construction of semantically sound com- putational cognitive models in artificial intelligence, computer and cognitive sci- ences. Over the last decades, results regarding the computation and learning of classical reasoning within neural networks have been promising. Nonetheless, there still remains much do be done. Artificial intelligence, cognitive and com- puter science are strongly based on several non-classical reasoning formalisms, methodologies and logics. In knowledge representation, distributed systems, hard- ware design, theorem proving, systems specification and verification classical and non-classical logics have had a great impact on theory and real-world ap- plications. Several challenges for neural-symbolic computation are pointed out, in particular for classical and non-classical computation in connectionist systems. We also analyse myths about neural-symbolic computation and shed new light on them considering recent research advances.
Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms. We propose two -norm regularized maximum likelihood estimators to learn the model parameters from i.i. d. samples. The first one is a joint MLE estimator which estimates all the parameters simultaneously. The second one is a node-wise conditional MLE estimator which estimates the parameters for each node individually. For both estimators, statistical analysis shows that under mild conditions the extra flexibility gained by the additive exponential family models comes at almost no cost of statistical efficiency. A Monte-Carlo approximation method is developed to efficiently optimize the proposed estimators. The advantages of our estimators over Gaussian graphical models and Nonparanormal estimators are demonstrated on synthetic and real data sets.
Methods for a Population-Based Study of Myopia and Other Eye Conditions in School Children: The Sydney Myopia Study

Purpose: The Sydney Myopia Study will establish the prevalence of myopia and other eye diseases in a large representative sample of Sydney school children. It will also examine the relationship between myopia and potential modifiable risk factors and will assess potential gene-environment interactions by examining parents and siblings. Methods: The target population is a stratified random cluster sample of 1750 Year 1 (age 6 years) and 1500 Year 7 (age 12 years) students from Sydney metropolitan schools. Procedures (comprehensive parent-administered questionnaire and examination) involve standardized protocols to allow for comparison with international population-based data. Examinations include a detailed assessment of visual acuity, cover testing for strabismus, identification of amblyopia, slit-lamp examination, non-contact ocular biometry and cycloplegia (cyclopentolate) followed by autorefraction, optical coherence tomography, retinal thickness measurement, digital mydriatic retinal photography and aberrometry. Conclusions: The Sydney Myopia Study design and methodology will ensure valid findings on ocular development and health in a large representative sample of Sydney school children, for comparison with other population-based refraction data.
Learning Phenotype Mapping for Integrating Large Genetic Data

Accurate phenotype mapping will play an important role in facilitating Phenome-Wide Association Studies (PheWAS), and potentially in other phenomics based studies. The Phe-WAS approach investigates the association between genetic variation and an extensive range of phenotypes in a high-throughput manner to better understand the impact of genetic variations on multiple phenotypes. Herein we define the phenotype mapping problem posed by PheWAS analyses, discuss the challenges, and present a machine-learning solution. Our key ideas include the use of weighted Jaccard features and term augmentation by dictionary lookup. When compared to string similarity metric-based features, our approach improves the F-score from 0.59 to 0.73. With augmentation we show further improvement in F-score to 0.89. For terms not covered by the dictionary, we use transitive closure inference and reach an F-score of 0.91, close to a level sufficient for practical use. We also show that our model generalizes well to phenotypes not used in our training dataset.
Security Analysis and Design of Proxy Signature Schemes over Braid Groups

The braid groups have attracted much attention as a new platform of constructing cryptosystems. This paper firstly analyzes the security vulnerabilities of existing proxy signature schemes over braid groups and presents feasible attacks. Then a new proxy signature scheme is proposed based on the difficulty of the conjugacy search problem and the multiple conjugacy search problem. Security analysis shows that the proposed scheme satisfies the security requirements of proxy signature.
Recognizing multiple persons' facial expressions using HMM based on automatic extraction of significant frames from image sequences

A method that can be used for recognizing facial expressions of multiple persons is proposed. In this method, the condition of facial muscles is assigned to a hidden state of a HMM for each expression. Then, the probability of the state is updated according to a feature vector obtained from image processing. Image processing is performed in two steps. First, a velocity vector is estimated from every two successive frames by using an optical flow algorithm. Then, a two-dimensional Fourier transform is applied to a velocity vector field at the regions around an eye and the mouth. The coefficients for lower frequencies are selected to form a feature vector. A mixture density is used for approximating the output probability of the HMM so as to represent a variation in facial expressions among persons. To cope with the case when two expressions are displayed continuously, the HMM computation is modified such that when the peak of a facial motion is detected, a new sequence of facial expressions is assumed to start from the previous frame with minimal facial motion. Experiments show that a mixture density is effective because the recognition accuracy improves as the number of mixtures increases. In addition, the method correctly recognizes a facial expression that continuously follows another one.
Induction of labour after fetal death: a randomized controlled trial of two prostaglandin regimens

A total of 85 women with antepartum fetal death between 14 and 42 weeks gestation was randomly assigned to one of two regimens of intravenous infusion of the prostaglandin analogue 16‐phenoxy‐17, 18, 19, 20‐tetranor‐PGE2‐methylsulphonamide (sulprostone) for inducing labour. Women received either 1 μg/min until delivery or the commonly recommended treatment of 1500 μg in 8 h followed by another, identical course of treatment if delivery did not occur within 24 h. The 1 μg/min dose schedule used half the amount of prostaglandin and resulted in statistically significantly fewer gastrointestinal side‐effects compared with the conventional treatment. All women were delivered vaginally and there were no differences in induction‐to‐delivery intervals between the two treatments. Sulprostone infused at a rate of 1 μg/min resulted in a 50% chance of being delivered within 12 h and a 90% chance of being delivered within 24 h, with an overall frequency of side‐effects of 20%.
Fast and Robust Adaptation of DFT-Domain Volterra Filters in Diagonal Coordinates Using Iterated Coefficient Updates

This paper presents a novel data-reusing technique for implementing adaptive discrete Fourier transform (DFT) domain Volterra filters in diagonal coordinates having arbitrary nonlinear order. In general, a major drawback of such nonlinear filters is the large number of parameters. Thus, a weak excitation of higher-order kernels results in a slow convergence for system identification tasks. In order to exploit the available innovation of the signals more effectively, each data frame is processed for a specified number of iterations in an overlap-save scheme, thereby enhancing the convergence performance. Due to inherent recursions of the repeated filtering and updating steps, this concept also lends itself to a fast algorithm, requiring only a moderate increase in algorithmic complexity over a baseline implementation. A detailed comparison of the required number of multiplications is given for several different algorithm versions. Despite the relatively simple concept, the outlined iteration algorithm exhibits significant gains in both adaptation speed and steady-state convergence. This is demonstrated by various experiments for both stationary noise and speech input considering an application of nonlinear acoustic echo cancellation. Finally, investigations on the tracking behavior for time-variant nonlinearities and robustness against misdetection of double-talk situations confirm the promising benefits and good trade-off of the proposed iterated coefficient updates Volterra filters approach.
Throughput and delay analysis of the IEEE 802.11e EDCA saturation

In this paper, we introduce a simple model for the enhanced distributed channel access (EDCA) mechanism under saturation conditions. This model captures the operation of the AIFS (arbitration inter-frame space) and contention window differentiation of the EDCA mechanism. Using this model, we analyze the throughput and delay performance of EDCA. The results of our analytical model are then verified using simulations.
Late-Time Spectroscopy of SN 2002cx: The Prototype of a New Subclass of Type Ia Supernovae

We present Keck optical spectra of SN 2002cx, the most peculiar known Type Ia supernova (SN Ia), taken 227 and 277 days past maximum light. Astonishingly, the spectra are not dominated by the forbidden emission lines of iron that are a hallmark of thermonuclear SNe in the nebular phase. Instead, we identify numerous P Cygni profiles of Fe II at very low expansion velocities of ∼700 km s-1, which are without precedent in SNe Ia. We also report the tentative identification of low-velocity O I in these spectra, suggesting the presence of unburned material near the center of the exploding white dwarf. SN 2002cx is the prototype of a new subclass of SNe Ia, with spectral characteristics that may be consistent with recent pure deflagration models of Chandrasekhar-mass thermonuclear SNe. These are distinct from the majority of SNe Ia, for which an alternative explosion mechanism, such as a delayed detonation, may be required.
Nonlinear directionally selective subunits in complex cells of cat striate cortex.

1. We have analyzed receptive fields (RFs) of directionally selective (DS) complex cells in the striate cortex of the cat. We determined the extent to which the DS of a complex cell depends on spatially identifiable subunits within the RF by studying responses to an optimally oriented, three-luminance-valued, gratinglike stimulus that was spatiotemporally randomized. 2. We identified subunits by testing for nonlinear spatial RF interactions. To do this, we calculated Wiener-like kernels in a spatial superposition test that depended on two RF positions at a time. The spatial and temporal separation of light and dark bars at these two positions varied over a spatial range of 8 degrees and a temporal range of +/- 112 ms in increments of 0.5 degree and 16 ms, respectively. 3. DS responses in complex cells cannot be explained by their responses to single light or dark bars because any linear superposition of responses whose time course is uniform across space shows no directional preference. 4. Nonlinear interactions between a flashed reference bar that is fixed in position and a second bar that is flashed at surrounding positions help explain DS by showing multiplicative-type facilitation for bar pairs that mimic motion in the preferred direction and suppression for bar pairs that mimic motion in the null direction. Interactions in the preferred direction have an optimal space/time ratio (velocity), exhibited by elongated, obliquely oriented positive domains in a space-time coordinate frame. This relationship is inseparable in space-time. The slope of the long axis specifies the preferred speed, and its negative agrees with the most strongly suppressed speed in the opposite direction. 5. When the reference bar position is moved across the RF, the spatiotemporal interaction moves with it. This suggests the existence of a family of nearly uniform subunits distributed across the RF. We call the subunit interaction, as averaged across the RF, the "motion kernel" because its spatial and temporal variables are those necessary to specify the velocity, the only parameter that distinguishes a moving image from a temporally modulated stationary image. The nonlinear interaction shows a spatial periodicity, which suggests a mechanism of velocity selectivity for moving extended images. (ABSTRACT TRUNCATED AT 400 WORDS)