Miracle-dz/UniFAR-FTUs huggingface dataset:
Kainate Receptors Mediate Signaling in Both Transient and Sustained OFF Bipolar Cell Pathways in Mouse Retina

A fundamental question in sensory neuroscience is how parallel processing is implemented at the level of molecular and circuit mechanisms. In the retina, it has been proposed that distinct OFF cone bipolar cell types generate fast/transient and slow/sustained pathways by the differential expression of AMPA- and kainate-type glutamate receptors, respectively. However, the functional significance of these receptors in the intact circuit during light stimulation remains unclear. Here, we measured glutamate release from mouse bipolar cells by two-photon imaging of a glutamate sensor (iGluSnFR) expressed on postsynaptic amacrine and ganglion cell dendrites. In both transient and sustained OFF layers, cone-driven glutamate release from bipolar cells was blocked by antagonists to kainate receptors but not AMPA receptors. Electrophysiological recordings from bipolar and ganglion cells confirmed the essential role of kainate receptors for signaling in both transient and sustained OFF pathways. Kainate receptors mediated responses to contrast modulation up to 20 Hz. Light-evoked responses in all mouse OFF bipolar pathways depend on kainate, not AMPA, receptors.
Squirmers with swirl: a model for Volvox swimming

Colonies of the green alga Volvox are spheres that swim through the beating of pairs of flagella on their surface somatic cells. The somatic cells themselves are mounted rigidly in a polymeric extracellular matrix, fixing the orientation of the flagella so that they beat approximately in a meridional plane, with axis of symmetry in the swimming direction, but with a roughly $20^{\circ }$ azimuthal offset which results in the eponymous rotation of the colonies about a body-fixed axis. Experiments on colonies of Volvox carteri held stationary on a micropipette show that the beating pattern takes the form of a symplectic metachronal wave (Brumley et al. Phys. Rev. Lett., vol. 109, 2012, 268102). Here we extend the Lighthill/Blake axisymmetric, Stokes-flow model of a free-swimming spherical squirmer (Lighthill Commun. Pure Appl. Maths, vol. 5, 1952, pp. 109–118; Blake J. Fluid Mech., vol. 46, 1971b, pp. 199–208) to include azimuthal swirl. The measured kinematics of the metachronal wave for 60 different colonies are used to calculate the coefficients in the eigenfunction expansions and hence predict the mean swimming speeds and rotation rates, proportional to the square of the beating amplitude, as functions of colony radius. As a test of the squirmer model, the results are compared with measurements (Drescher et al. Phys. Rev. Lett., vol. 102, 2009, 168101) of the mean swimming speeds and angular velocities of a different set of 220 colonies, also given as functions of colony radius. The predicted variation with radius is qualitatively correct, but the model underestimates both the mean swimming speed and the mean angular velocity unless the amplitude of the flagellar beat is taken to be larger than previously thought. The reasons for this discrepancy are discussed.
Trading Latency for Performance in Data-Intensive Applications

Preston Briggs is a Senior Engineer at Reservoir Labs and an Affiliate Professor in Computer Science and Engineering at the University of Washington. He received a PhD in computer science from Rice University in 1992. preston@cs.washington. edu The rising importance of data-intensive applications has fueled the growth of a plethora of distributed computing frameworks, including Hadoop, Spark, and GraphLab. We have developed a system called Grappa [1, 2] to aid programmers in developing new frameworks. Grappa provides a distributed shared memory abstraction to hide complexity from the programmer, and takes advantage of parallelism in the data to hide remote access latency and to trade latency for more performance. These techniques allow it to outperform existing frameworks by up to an order of magnitude.
The Open University ’ s repository of research publications and other research outputs Lexicon generation by extraction of context patterns

Semantic browser technologies such as Magpie require the construction of lexicons to support the identification of terms in Web pages which are linked to a user’s chosen ontology. We frame the generation of such lexicons from ontologies as a problem of finding synonyms and hyponyms. Synonym finding using the hypothesis of semantic substitutability relies upon the discovery of patterns in which the target word occurs. Information extraction has the potential to find a range of patterns in text. We present a methodology for finding synonyms for inclusion in lexicons in this way and preliminary tests of the method using standard tools.
Painlevé IV with both Parameters Zero: A Numerical Study

The six Painlevé equations were introduced over a century ago, motivated by rather theoretical considerations. Over the last several decades, these equations and their solutions, known as the Painlevé transcendents, have been found to play an increasingly central role in numerous areas of mathematical physics. Due to extensive dense pole fields in the complex plane, their numerical evaluation remained challenging until the recent introduction of a fast “pole field solver” [ 1 ]. The fourth Painlevé equation has two free parameters in its coefficients, as well as two free initial conditions. The present study applies this new computational tool to the special case when both of its parameters are zero. We confirm existing analytic and asymptotic knowledge about the equation, and also explore solution regimes which have not been described in the previous literature.
The calibration of read-out-streak photometry in the XMM-Newton Optical Monitor and the construction of a bright-source catalogue

The dynamic range of the XMM-Newton Optical Monitor (XMM-OM) is limited at the bright end by coincidence loss, the superposition of multiple photons in the individual frames recorded from its micro-channel-plate (MCP) intensified charge-coupled device (CCD) detector. One way to overcome this limitation is to use photons that arrive during the frame transfer of the CCD, forming vertical read-out streaks for bright sources. We calibrate these read-out streaks for photometry of bright sources observed with XMM-OM. The bright source limit for read-out streak photometry is set by the recharge time of the MCPs. For XMM-OM we find that the MCP recharge time is 0.55 ms. We determine that the effective bright limits for read-out streak photometry with XMM-OM are approximately 1.5 magnitudes brighter than the bright source limits for normal aperture photometry in full-frame images. This translates into bright-source limits in Vega magnitudes of UVW2=7.1, UVM2=8.0, UVW1=9.4, U=10.5, B=11.5, V=10.2 and White=12.5 for data taken early in the mission. The limits brighten by up to 0.2 magnitudes, depending on filter, over the course of the mission as the detector ages. The method is demonstrated by deriving UVW1 photometry for the symbiotic nova RR Telescopii, and the new photometry is used to constrain the e-folding time of its decaying UV emission. Using the read-out streak method, we obtain photometry for 50 per cent of the missing UV source measurements in version 2.1 of the XMM-Newton Serendipitous UV Source Survey (XMM-SUSS 2.1) catalogue.
Generalized Reinforcement Learning for Manipulation Skills – Combining Low-dimensional Bayesian Optimization with High-dimensional Motion Optimization

—This paper addresses the problem of how a robot can autonomously improve a manipulation skill in an efﬁcient and secure manner. Instead of using the standard reinforcement learning formulation where all objectives are deﬁned in a single reward function, we propose a generalized formulation that consists of three components: 1) A known analytic cost function; 2) A black-box reward function; 3) A black-box binary success constraint. While optimization of the analytic cost function is inherently high-dimensional, in typical robot manipulation problems we may assume that the black-box reward and constraint only depend on a lower dimensional projection of the policy. With our formulation we can exploit this structure and propose a sample-efﬁcient learning framework that iteratively improves the skill with respect to the objective functions under the condition that the success constraint is fulﬁlled. The analytic cost function is optimized with motion optimization methods over the high dimensional policy where the lower dimensional parameters are ﬁxed. The black-box reward is optimized with constraint Bayesian optimization over the lower-dimensional parameter. During both improvement steps the success constraint is used to keep the optimization in a secure region and to clearly distinguish between motions that lead to success or failure. The learning algorithm is evaluated on simulated benchmark problems and real-world tasks like opening a door with a PR2.
- 291-Family and Population Differences in Muscle Fibre Recruitment in Farmed Atlantic Salmon ( Salmo salar )

The number of muscle fibres recruited to reach a given body weight in Atlantic salmon (Salmo salar L.) showed significant variation between two strains with different growth rates and life history characteristics. Patterns of fibre recruitment also varied between twoyear classes of the same strain grown under different production conditions. The maximum diameter of fast muscle fibres was around 240 μm in all cases. The density of muscle fibres decreased with increasing body weight, although average values were relatively constant for fish of 3.5 to 6.5 kg. The variation in muscle fibre density within a family, often 65 to 140 fibres mm muscle, was significantly greater than that between families and/or strains. The significance of this variation in fibre recruitment for the textural and processing characteristics of the flesh is discussed.
Improved Audio Classification Using a Novel Non-Linear Dimensionality Reduction Ensemble Approach

Two important categories of machine learning methodologies have recently attracted much interest in classification research and its applications. On one side, unsupervised and semi-supervised learning allow to benefit from the availability of larger sets of training data, even if not fully annotated with class labels, and of larger sets of diverse feature representations, through novel dimensionality reduction schemes. On the other side, ensemble methods allow to benefit from more diversity in base learners though larger data and feature sets. In this paper, we propose a novel ensemble learning approach making use of recent non-linear dimensionality reduction methods. More precisely, we apply t-SNE (t-distributed Stochastic Neighbor Embedding) to a large feature set to come up with embeddings of various dimensionality. A k-NN classifier is then obtained for each embedding, leading to an ensemble whose estimates can then be combined, making use of various ensemble combination rules from the literature. The rationale of this approach resides in its potential capacity to better handle manifolds of different dimensionality in different regions of the feature space. We evaluate the approach on a transductive audio classification task, where only part of the whole data set is labeled. We confirm that dimensionality reduction by itself can improve performance (by 40% relative), and that creating an ensemble through the proposed approach further reduces classification error rate by about 10% relative.
Microstructure and Thermal Conductivity of Hydrated Calcium Silicate Board Materials

The thermal conductivity of a porous material is controlled by the thermal conductivities of its components and their spatial arrangement within the composite structure, e.g., the material's microstructure. In this study, the relationships between thermal conductivity and microstructural parameters, such as porosity and pore size are examined for two calcium silicate boards of different densities. Thermal conductivities are measured from room temperature to 400°C using a transient plane source technique, for both the as-received boards and for a corresponding set of boards that were first heated to and held at 1000°C for at least 4 h. Microstructure is characterized by the measurement of the boards' bulk and powder densities and an assessment of pore size based on scanning electron microscopy. The experimentally measured thermal conductivities are then compared to those predicted by three previously presented theories for porous materials. A better agreement is observed between the experimental values and two of the three theories. Aging the boards at 1000°C has a small but significant effect on thermal conductivity, decreasing the room temperature values but increasing the 400°C ones.
Appendix : Proximal Quasi-Newton for Computationally Intensive ` 1-regularized M-estimators

To exploit the CNSC-T property, we first re-build our problem and algorithm on the reduced space Z = {z ∈ R|z = Uw}, where the strong-convexity property holds. Then we prove the asymptotic super-linear convergence on Z under the condition that the inner problem is solved exactly and no shrinking strategy is not applied. Finally we prove the objective (1) is bounded by the difference between current iterate and the optimal solution. In Section 1.5, we provide the global convergence proof when the shrinking strategy is applied.
Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion

Real-time or online 3D reconstruction has wide applicability and receives further interest due to availability of consumer depth cameras. Typical approaches use a moving sensor to accumulate depth measurements into a single model which is continuously refined. Designing such systems is an intricate balance between reconstruction quality, speed, spatial scale, and scene assumptions. Existing online methods either trade scale to achieve higher quality reconstructions of small objects/scenes. Or handle larger scenes by trading real-time performance and/or quality, or by limiting the bounds of the active reconstruction. Additionally, many systems assume a static scene, and cannot robustly handle scene motion or reconstructions that evolve to reflect scene changes. We address these limitations with a new system for real-time dense reconstruction with equivalent quality to existing online methods, but with support for additional spatial scale and robustness in dynamic scenes. Our system is designed around a simple and flat point-Based representation, which directly works with the input acquired from range/depth sensors, without the overhead of converting between representations. The use of points enables speed and memory efficiency, directly leveraging the standard graphics pipeline for all central operations, i.e., camera pose estimation, data association, outlier removal, fusion of depth maps into a single denoised model, and detection and update of dynamic objects. We conclude with qualitative and quantitative results that highlight robust tracking and high quality reconstructions of a diverse set of scenes at varying scales.
A Test for Density Forecast Comparison with Applications to Risk Management

In this paper we propose a testing procedure for comparing the predictive abilities of possibly misspecified density forecast models. We use the minimum Kullback-Leibler Information Criterion (KLIC) divergence measure to define the distance between the candidate density forecast model and the true model. We use the fact that the inverse-normal transform of the probability integral transforms (PIT) should be IID standard normal as discussed in Berkowitz (2001). To compare the performance of density forecast models in the tails, we use the censored likelihood functions to compute the tail minimum KLIC. The reality check test of White (2000) is then constructed using our distance measure as a loss function. To highlight the merits of our approach, we use the daily S&P500 and NASDAQ return series to conduct an empirical density forecast comparison exercise. A large set of distributions, including some recently proposed flexible distributions to accommodate higher moments, and the ARCH-family volatility specifications are studied. Our empirical findings lend further support of fat-tailedness and skewness of return distributions. In addition, the choice of conditional distribution specification appears to be a much more dominant factor in determining the quality of density forecasts than the choice of volatility specification.
An Incentive Model of IS Outsourcing Contract

An approach to analyze incentive schemes and structuring IS outsourcing contracts for the mutual gain is presented in this paper. Outsourcing contract is critical to the management of the IS outsourcing relationship because improperly or incompletely written contracts can have significant negative implications for the firms. A linear principal-agent model based on EVA (economic value added) for constructing incentive contracts in IS outsourcing is developed. Incentive management literature relative to linear principal-agent model has focused on determining the proportion of agent's profit to principal's profit to receive maximum economic returns from agent's action. In fact, the validity of incentive is decided by the positive correlation between the variable of agent's performance and outsourcing output. Arguing the feasibility of using EVA to evaluate the outsourcing vendor's performance in IS outsourcing, a linear principal-agent model based on EVA is devised for improving IS outsourcing vendors' performance and the advantage and disadvantage of this model was discussed.
Between Extraordinary and Marginalized: Negotiating Tensions in Becoming Special Education-Certified Teachers

ABSTRACT The field of special education is in flux, with high shortages and attrition and increasing requirements around quality teachers and teaching contexts. In this study, we explore how preservice teachers develop identities as special education-certified teachers within this context. Data include focus groups, admissions essays, and class assignments from 3 years of a masters-level preparation program. Analysis began with open coding, and initial themes included positioning, power, and marginalization. Iterative analysis led to findings around negotiation—how preservice teachers negotiated what it meant to be a teacher in diverse teaching contexts while both maintaining a view of special education-certified teachers as extraordinary and also experiencing marginalization within their roles. Implications include supporting teacher candidates in managing these tensions, as well as a need for programs to partner with schools as a whole and not just individual cooperating teachers.
RAN as a service: Challenges of designing a flexible RAN architecture in a cloud-based heterogeneous mobile network

Small cell deployment is one of the most promising technology trends to cope with the rising need for very high data rates foreseen in future mobile networks. Nevertheless, it has the side effect of introducing more complex interference scenarios, needing to be properly managed by means of coordination mechanisms. The recently introduced Cloud-RAN architecture, based on centralized processing, enables the implementation of efficient interference prevention and cancelation algorithms across multiple cells. However, it struggles with the need of high-capacity front-haul links and cost-intensive deployments. This paper introduces the novel concept of RANaaS (Radio Access Network-as-a-Service) as a flexible architecture based on centralized processing, capable of handling the increasing interference in very dense networks, reducing energy consumption, cost-efficiently deploying and managing cellular networks.
Toward green PON with adaptive sleep mode

Passive optical network (PON) has been widely used in the access network around the world. High power consumption brings challenges and also opportunities for building a green network in the future. Setting ONUs into sleep state is the most cost-effective manner. Our research first focuses on the characteristics for a variety of sleep states, and then introduces a novel sleep mode which is more flexible in adjusting ONUs into suitable sleep states. Simulation results show that our design can save 57% energy on average and the time delay is around 1ms which can meet the requirements of most service.
Identification of Olive Ridley Turtle Using Feature Extraction

The present study deals with identifying the particular species of olive ridleys turtle which is an endangered species among seven sea turtles worldwide. The existing method used for identification of turtles is tracking and tagging. Currently, identification is made only for the single turtle by feature extraction using mat lab software tool which is cheaper while compared to the existing method (Tracking and Tagging). Above all in s this method the time is saved and also the identification can be done for the group of turtles.
What do citation counts measure? A review of studies on citing behavior

Purpose – The purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non‐scientific, reasons.Design/methodology/approach – The review covers research published from the early 1960s up to mid‐2005 (approximately 30 studies on citing behavior‐reporting results in about 40 publications). Findings – The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite. However, the results of t.
Travel Time Prediction on Highways

We describe the development of a predictive model for vehicle journey time on highways. Accurate travel time prediction is an important problem since it enables planning of cost effective vehicle routes and departure times, with the aim of saving time and fuel while reducing pollution. The main information source used is data from roadside double inductive loop sensors which measure vehicle speed, flow and density at specific locations. We model the spatiotemporal distribution of travel times by using local linear regression. The use of real-time data is very accurate for shorter journeys starting now and less reliable as journey times increase. Local linear regression can be used to optimally balance the use of historical and real time data. The main contribution of the paper is the extension of local linear models with higher order autoregressive travel time variables, namely vehicle flow data, and density data. Using two years of UK Highways Agency (HA) loop sensor data we found that the extended model significantly improves predictive performance while retaining the main benefits of earlier work: interpretability of linear models as well as computationally simple predictions.