Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutrophils Express Oncomodulin and Promote Optic Nerve Regeneration

Although neurons are normally unable to regenerate their axons after injury to the CNS, this situation can be partially reversed by activating the innate immune system. In a widely studied instance of this phenomenon, proinflammatory agents have been shown to cause retinal ganglion cells, the projection neurons of the eye, to regenerate lengthy axons through the injured optic nerve. However, the role of different molecules and cell populations in mediating this phenomenon remains unclear. We show here that neutrophils, the first responders of the innate immune system, play a central role in inflammation-induced regeneration. Numerous neutrophils enter the mouse eye within a few hours of inducing an inflammatory reaction and express high levels of the atypical growth factor oncomodulin (Ocm). Immunodepletion of neutrophils diminished Ocm levels in the eye without altering levels of CNTF, leukemia inhibitory factor, or IL-6, and suppressed the proregenerative effects of inflammation. A peptide antagonist of Ocm suppressed regeneration as effectively as neutrophil depletion. Macrophages enter the eye later in the inflammatory process but appear to be insufficient to stimulate extensive regeneration in the absence of neutrophils. These data provide the first evidence that neutrophils are a major source of Ocm and can promote axon regeneration in the CNS.
Predator-Miner: ad hoc mining of associations rules within a database management system

We present a prototype system, Predator-Miner, which extends Predator with an relational-like association rule mining operator to support data mining operations. Predator-Miner allows a user to combine association rule mining queries with SQL queries. This approach towards tight integration differs from existing techniques of using user-defined functions (UDFs), stored procedures, or re-expressing a mining query as several SQL queries in two aspects. First, by encapsulating the task of association rule mining in a relational operator, we allow association rule mining to be considered as part of the query plan, on which query optimization can be performed on the mining query holistically. Second, by integrating it as a relational operator, we can leverage on the mature field of relational database technology. We extend Predator to support a variant of DMQL, and allow SQL and DMQL to be intermixed in a query. We also demonstrate a cost-based mining query optimization framework.
Finding our bearings: Understanding public attitudes toward growing black bear populations in Alabama

ABSTRACT Black bear populations in Alabama have been low since the early part of the 20th century, but an increase in sightings over the past decade has led researchers to believe that populations may be growing, spurring discussions on restoring the species throughout its native range in the state. The rise of a large carnivore population is likely to increase human–wildlife interactions and attract outspoken opinions in support of and against population recovery. We conducted a mail survey of Alabama residents to access public views and opinions toward growing bear populations. Multiple linear regression was used to examine the degree to which residents’ attitudes toward black bears could be predicted by demographic, cognitive, and experiential variables. Results demonstrate that the strongest predictors of attitudes toward bears included mutualist and hunting beliefs, trust in the state wildlife management agency, race, and exposure to bear education materials.
Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep

There is an identified need for objective, reliable, and scalable methods of measuring and recording sleep. Such methods must be designed for easy integration into people's lives in order to support both sleep therapy and everyday personal informatics. This paper describes the design and evaluation of a mobile phone application to record sleep, the design of which has substantive foundation in clinical sleep research. Two user studies were carried out which demonstrate that the application produces valid measurements of sleep quality and high levels of usability, whilst not seriously disturbing sleep or the sleep environment. These findings suggest that the app is suitable for both everyday sleep monitoring in a personal informatics context, and for integration into sleep interventions.
Network completion via joint node clustering and similarity learning

In this study, we investigate the problem of network completion by considering the similarities between the node attributes. Given a sample of observed nodes with their incident edges, how can we efficiently reconstruct the network by completing the missing edges of unobserved nodes? Apart from the missing edges, in real settings the node attributes may be partially missing, as well as they may introduce noise when completing the network. We propose a network completion method based on joint clustering and similarity learning. The proposed approach differs from competitive strategies, which consider attribute-based similarities at the node-level. First we generate clusters based on the node attributes, thus reducing the noise and the sparsity in the case that the attributes may be missing. We design a joint objective function to jointly factorize the adjacency matrix of the observed edges with the cluster-based similarities of the node attributes, while at the same time the clusters are adapted, accordingly. In addition, we propose an optimization algorithm to solve the network completion problem via alternating minimization. Our experiments on two real world social networks from Facebook and Google+ show that the proposed approach achieves high completion accuracy, compared to other state-of-the-art methods.
Shan‐and‐Chen‐type multiphase lattice Boltzmann study of viscous coupling effects for two‐phase flow in porous media

In this paper, the Shan–Chen‐type (SC) multiphase lattice Boltzmann model was used to study the viscous coupling effects for immiscible two‐phase flow in porous media. In the model, any typical equation of state can be incorporated and different contact angles of the gas–liquid interface at a solid wall can be obtained easily through adjusting the ‘density of wall’ (Benzi et al., Phys. Rev. E 2006; 74(2):021509). The viscous coupling effects due to capillary number, the viscosity ratio and the wetting angle were investigated. The two‐phase flows with density ratio as high as 56 in porous media were simulated. For different viscosity ratios and wettability, two‐phase flow patterns and relative‐permeability curves as a function of wetting saturation were obtained. It is observed that when the wetting phase is less viscous and covers the solid surface, the relative permeability of the non‐wetting phase may be greater than unity. Here, the SC model is demonstrated as a suitable tool to study the immiscible two‐phase flow in porous media because it is simple, easy to get the desired contact angle and able to simulate immiscible phase flow with high‐density ratio. Copyright © 2008 John Wiley & Sons, Ltd.
System Effects: A Hybrid Methodology for Exploring the Determinants of Food In/Security

Household food insecurity is the product of a wide range of environmental, social, and economic determinants, which themselves interact with and affect one another. On this point, though, much of the existing food security scholarship suffers from a lack of theoretical sophistication and tends to neglect the complex nature of the urban foodscape. This article develops an original systematic mixed method for understanding the determinants of food security, grounded in a new theoretical framework that integrates complex systems theory and the capability approach. Both theory and method have been developed by reference to a comparative empirical study of Afghan migrant communities in Sydney, London, and San Francisco. The efficacy of this (re)theorization and its accompanying system effects method are demonstrated by way of a worked example of their use in the case of the Sydney Afghan community.
An iterative analysis of single-hop B-MAC networks under Poisson traffic

The Berkeley-medium access control (B-MAC) is a lightweight, configurable and asynchronous duty cycle medium access control (MAC) protocol in wireless sensor networks. This article presents an analytic modelling of single-hop B-MAC protocol under a Poisson traffic assumption. Our model considers important B-MAC parameters such as the sleep cycle, the two stage backoff mechanism, and the extended preamble. The service delay of an arriving packet and the energy consumption are calculated by an iterative method. The simulation results verify that the proposed analytic model can accurately estimate the performance of single-hop B-MAC with different operating environments.
Auditory and visual word processing studied with fMRI

Brain activations associated with semantic processing of visual and auditory words were investigated using functional magnetic resonance imaging (fMRI). For each form of word presentation, subjects performed two tasks: one semantic, and one nonsemantic. The semantic task was identical for both auditory and visual presentation: single words were presented and subjects determined whether the word was concrete or abstract. In the nonsemantic task for auditory words, subjects determined whether the word had one syllable or multiple syllables. In the nonsemantic task for visual words, subjects determined whether the word was presented in lower case or upper case.
Shared Paged Binding Array: A Universal Datastructure for Parallel Logic Programming

Two major problems that arise in parallel logic programming systems are: (i) redundant computation during and-parallel execution of dependent goals, and, (ii) eecient representation of multiple environments at runtime. Both these problems are caused by non-determinism present in logic programs|responsible for much of the power of logic programming. This paper is mainly concerned with solving the second problem, namely, the eecient representation of multiple environments at runtime in parallel logic programming systems. We present a datastructure called the Shared Paged Binding Array that arguably is most suited for implementing any arbitrary parallel logic programming system (i.e., a system that exploits any arbitrary combination of dependent-and parallelism, independent and-parallelism and or-parallelism). This datastructure can also be used for and-or parallel execution of Committed Choice Languages with Deep Guards as well as for realizing the implementations of more advanced models of parallel execution such as the Extended Andorra Model and the Andorra Kernel Language. Details of the Shared Paged Binding Array are presented, its merits over other techniques are shown, and, nally, implementations that combine different avors of and-and or-parallelism using Shared Paged Binding Arrays are brieey sketched.
Delay-Sensitive Branch Predictors for Future Technologies DRAFT

Accurate branch prediction is an essential component of a modern, deeply pipelined microprocessors. Because the branch predictor is on the critical path for fetching instructions, it must deliver a prediction in a single cycle. However, as feature sizes shrink and clock rates increase, access delay will significantly decrease the size and accuracy of branch predictors that can be accessed in a single cycle. Thus, there is a tradeoff between branch prediction accuracy and latency. Deeper pipelines improve overall performance by allowing more aggressive clock rates, but some performance is lost due to increased branch misprediction penalties. Ironically, with shorter clock periods, the branch predictor has less time to make a prediction and might have to be scaled back to make it faster, which decreases accuracy and reduces the advantage of higher clock rates. We propose several methods for breaking the tradeoff between accuracy and latency in branch predictors. Our methods fall into two broad categories: hierarchical predictors using purely hardware implementations, and cooperative predictors that off-load some prediction work to the compiler. We describe hierarchical organizations that extend traditional predictors. We then describe a highly accurate branch predictor based on a neural learning technique. Using a hierarchical organization, this complex multi-cycle predictor can be used as a component of a fast delay-sensitive predictor. We introduce a novel cooperative branch predictor that off-loads most of the prediction work to the compiler with profiling. The compiler communicates profiled information to the microprocessor using extensions to the instruction set. This Boolean formula predictor has a small and fast hardware implementation, and will work in less than one cycle in even the smallest technologies with the most aggressive projected clock rates. Finally, we present another cooperative technique, branch path re-aliasing, that moves complexity off of the critical path for making a prediction and into the compiler; this technique increases accuracy by reducing destructive aliasing during the less critical update stage.
Strong iISS: Combination of iISS and ISS with respect to small inputs

This paper studies the notion of Strong iISS, which imposes both integral input-to-state stability (iISS) and input-to-state stability (ISS) with respect to small inputs. This combination characterizes the robustness property, exhibited by many practical systems, that the state remains bounded as long as the magnitude of exogenous inputs is reasonably small but may diverge for stronger disturbances. We provide three Lyapunov-type sufficient conditions for Strong iISS. One is based on iISS Lyapunov functions admitting a radially non-vanishing (class K) dissipation rate. However we show that it is not a necessary condition for Strong iISS. Two less conservative conditions are then provided, which are used to demonstrate that asymptotically stable bilinear systems are Strongly iISS. Finally, we discuss cascade and feedback interconnections of Strong iISS systems.
On the sustainability of web systems evolution

In the last twenty years, the evolution of web systems has been driven along three dimensions: the processes used to develop, evolve, maintain and re-engineer the systems themselves; the end products (the pages, content and links) of such processes; and finally the people dimension, with the extraordinary shift in how developers and users shape, interact and maintain the code and content that they put online. This paper reviews the questions that each of these dimensions has addressed in the past, and indicates which ones will need to be addressed in the future, in order for web system evolution to be sustainable. We show that the study on websites evolution has shifted from server- to client-side, focusing on better technologies and processes, and that the users becoming creators of content open several open questions, in particular the issue of credibility of the content created and the sustainability of such resources in the long term.
A Synchrophasor Based Optimal Voltage Control Scheme with Successive Voltage Stability Margin Improvement

This paper proposes an optimal control scheme based on a synchronized phasor (synchrophasor) for power system secondary voltage control. The framework covers voltage stability monitoring and control. Specifically, a voltage stability margin estimation algorithm is developed and built in the newly designed adaptive secondary voltage control (ASVC) method to achieve more reliable and efficient voltage regulation in power systems. This new approach is applied to improve voltage profile across the entire power grid by an optimized plan for VAR (reactive power) sources allocation; therefore, voltage stability margin of a power system can be increased to reduce the risk of voltage collapse. An extensive simulation study on the IEEE 30-bus test system is carried out to demonstrate the feasibility and effectiveness of the proposed scheme.
An Approach to Analyse Facial Expression from Videos using Pyramid Histogram of Orientation Gradients

Recognition of facial expression from video sequence is one of the challenge in pattern recognition. It has the advantage of using the previous frame and next frame of video sequence. In this paper a novel method (PHOG) is proposed which extract the features in the form of feature vector and then feed to the classifier for classification in there abstract classes i.e. happy, sad, fear, angry and surprise. Prior to this method a novel method is used for key frame detection. This algorithm differs from conventional one mainly with the used of image segmentation. The experiments were performed on the standard dataset which is taken from GEMEPFERA 2011 competition. The comparison between the PHOG and Gabor features using the GEMEP-FERA dataset implies that PHOG(pyramid histogram of orientation gradients) with shorter feature vector length could achieve a better accuracy than Gabor feature. For expression classification we use support vector machine (SVM) and compare the results to the previously performed in FERA 2011 emotion challenge baseline.
The underlying processes of Cross Race Facial Recognition :social motivation versus perceptual expertise.

This research was conducted to examine which processes underlie the mistakes individuals make when it comes to Cross Race Facial Recognition. Individuals tend to have better recognition for same race faces than other race faces, an effect called the Cross Race Effect. The two processes that were examined were social motivation and perceptual expertise. Social motivation was manipulated by means of social exclusion. Perceptual expertise was manipulated by means of a question about what emotion the participants saw in the faces during a confrontation. It was expected that individuals who are socially excluded have a higher need to belong in general, the results support this assumption. For perceptual expertise, it was expected that individuals who focus on emotion in encoding a face are better at recognizing other race-faces afterwards than individual who do not focus on emotion. Instead, the opposite happened; individuals who focus on emotion were better at recognizing same race faces than individuals who do not focus on emotion. Results suggest that people who focus on emotion have lower false alarm rates only for other race faces. This research is a good starting point for future research about the processes that could explain the Cross Race Effect.
Binding of the proline-rich segment of myelin basic protein to SH3 domains: spectroscopic, microarray, and modeling studies of ligand conformation and effects of posttranslational modifications.

Myelin basic protein (MBP) is a multifunctional protein involved in maintaining the stability and integrity of the myelin sheath by a variety of interactions with membranes and with cytoskeletal and other proteins. A central segment of MBP is highly conserved in mammals and consists of a membrane surface-associated amphipathic alpha-helix, immediately followed by a proline-rich segment that we hypothesize is an SH3 ligand. We show by circular dichroic spectroscopy that this proline-rich segment forms a polyproline type II helix in vitro under physiological conditions and that phosphorylation at a constituent threonyl residue has a stabilizing effect on its conformation. Using SH3 domain microarrays, we observe that the unmodified recombinant murine 18.5 kDa MBP isoform (rmC1 component) binds the following SH3 domains: Yes1 > PSD95 > cortactin = PexD = Abl = Fyn = c-Src = Itk in order of decreasing affinity. A quasi-deiminated form of the protein (rmC8) binds the SH3 domains Yes1 > Fyn > cortactin = c-Src > PexD = Abl. Phosphorylation of rmC1 at 1-2 threonines within the proline-rich segment by mitogen-activated protein kinase in vitro has no effect on the binding specificity to the SH3 domains on the array. An SH3 domain of chicken Fyn is also demonstrated to bind to lipid membrane-associated C1, phosphorylated C1, and rmC8. Molecular docking simulations of the interaction of the putative SH3 ligand of classic MBP with the human Fyn SH3 domain indicate that the strength of the interaction is of the same order of magnitude as with calmodulin and that the molecular recognition and association is mediated by some weak CH.pi interactions between the ligand prolyl residues and the aromatic ones of the SH3 binding site. One such interaction is well-conserved and involves the stacking of an MBP-peptide prolyl and an SH3 domain tryptophanyl residue, as in most other SH3-ligand complexes. Lysyl and arginyl residues in the peptide canonically interact via salt bridges and cation-pi interactions with negatively charged and aromatic residues in the SH3 domain binding site. Posttranslational modifications (phosphorylation or methylation) of the ligand cause noticeable shifts in the conformation of the flexible peptide and its side chains but do not predict any major inhibition of the binding beyond somewhat less favorable interactions for peptides with phosphorylated seryl or threonyl residues.
Detecting faulty wireless sensor nodes through Stochastic classification

In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an analysis of the sensor readings, thus avoiding additional control overhead. In particular, abnormal behavior of a sensor node will be inferred by analyzing the spatiotemporal correlation of its data with respect to its neighborhood. The algorithm is tested on a public dataset, over which different classes of faults were artificially superimposed.
Supporting multiple classes of services in IP over WDM networks

We consider the next generation optical Internet, where direct communication channels between IP entities are provided through an all-optical WDM layer. Since current IP provides only best effort service, supporting quality of service (QoS) has become a crucial issue. We propose a simple, yet efficient priority scheme to support basic QoS at the WDM layer of the optical Internet. Unlike existing approaches that depend on buffers (queues) and scheduling algorithms (e.g., fair queuing), our scheme does not mandate any buffer in the WDM layer in order to differentiate the services. The proposed scheme is based on the concept of using an offset time between a control (set-up) packet and the corresponding data burst as in optical burst switching (OBS). More specifically, by assigning different offset times to different classes, it is possible for a higher priority class to be isolated from lower priority classes. We quantify the offset time required for class isolation, and analyze the blocking probability of each class.
X marks the spot

This essay critically assesses the shift from Latina/o to Latinx to articulate new formations of language, history, and cultural politics. Taking cue from Suzanne Oboler’s foundational 1995 book Ethnic Labels, Latino Lives: Identity and the Politics of (Re)Presentation in the United States, it further asks what’s at stake in adopting new labels and discarding those preceding them, particularly for attending to the lives and histories of individuals incessantly impacted by matters related to gender and sexuality. While recognizing the important representational work the X does, the essay also troubles an easy embrace of it by asking what the X crosses out or eliminates from consideration within contemporary identity politics.