Miracle-dz/UniFAR-FTUs huggingface dataset:
' s personal copy Ca 2 ENTRY THROUGH A NON-SELECTIVE CATION CHANNEL IN APLYSIA BAG CELL NEURONS

Ca influx through voltage-gated Ca channels is a fundamental signaling event in neurons; however, nontraditional routes, such as non-selective cation channels, also permit Ca entry. The present study examines the Ca permeability of a cation channel that drives an afterdischarge in Aplysia bag cell neurons. The firing of these neurons induces peptide release and reproduction. Single channelcontaining inside-out patches excised from cultured bag cell neurons, with the cytoplasmic face bathed in K -aspartate and the extracellular face bathed in artificial seawater (11 mM Ca ), had a reversal potential near 50 mV. In keeping with Ca permeability, this was right-shifted to approximately 60 mV in high Ca (substituted for Mg ) and left-shifted to around 40 mV in zero Ca (replaced with Mg ). The current showed inward rectification between 30 and 90 mV, and a conductance of 29 pS in normal Ca , 30 pS in high Ca , 32 pS in Ba (substituted for Ca ), but only 21 pS in zero Ca . Despite a greater conductance in Ba , the channel did not display anomalous mol fraction in an equimolar Ca –Ba mix. Eliminating internal Mg lowered activity, but did not alter inward rectification, suggesting intracellular Mg is a fast, voltage-independent blocker. Imaging bag cell neurons in Mn saline (substituted for Ca ) revealed enhanced fura-quench following cation channel activation, consistent with Mn permeating as a Ca surrogate. Finally, triggering the cation channel while tracking capacitance revealed a Ca -dependent increase in membrane surface area, consistent with vesicle fusion. Thus, the cation channel not only drives the afterdischarge, but also passes Ca to potentially initiate secretion. In general, this may represent an alternate means by which neurons elicit neuropeptide release. © 2009 IBRO. Published by Elsevier Ltd. All rights reserved.
NCCloud: applying network coding for the storage repair in a cloud-of-clouds

To provide fault tolerance for cloud storage, recent studies propose to stripe data across multiple cloud vendors. However, if a cloud suffers from a permanent failure and loses all its data, then we need to repair the lost data from other surviving clouds to preserve data redundancy. We present a proxy-based system for multiple-cloud storage called NCCloud, which aims to achieve cost-effective repair for a permanent single-cloud failure. NCCloud is built on top of network-coding-based storage schemes called regenerating codes. Specifically, we propose an implementable design for the functional minimum-storage regenerating code (F-MSR), which maintains the same data redundancy level and same storage requirement as in traditional erasure codes (e.g., RAID-6), but uses less repair traffic. We implement a proof-of-concept prototype of NCCloud and deploy it atop local and commercial clouds. We validate the cost effectiveness of FMSR in storage repair over RAID-6, and show that both schemes have comparable response time performance in normal cloud storage operations.
CommunityExplorer: A Framework for Visualizing Collaboration Networks

Understanding the network of collaborations, identifying the key players, potential future collaborators, and trends in the field are very important to carry out a project successfully. In this paper, we present CommunityExplorer, a visualization framework that facilitates presenting, exploring, and understanding the network of collaborations at once. The framework performs data extraction, parsing, and modeling automatically. It is easy to adopt and utilizes a bigraph visualization that scales well.We demonstrate the advantage of CommunityExplorer to identify the collaboration of authors on 346 and 104 research papers published in SOTFVIS/VISSOFT and IWST communities respectively. We found that even though SOFTVIS/VISSOFT has more contributors, IWST exhibits more collaboration.We discovered that contributors in IWST are more resilient than those in SOFTVIS/VISSOFT, which are more volatile. Moreover, collaboration in IWST is concentrated in a single large group, while in SOFTVIS/VISSOFT it is spread among many tiny groups and a few medium-sized ones.
The Promise and Challenge of Stochastic Computing

Stochastic computing (SC) is an unconventional method of computation that treats data as probabilities. Typically, each bit of an <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-bit stochastic number (SN) <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula> is randomly chosen to be 1 with some probability <inline-formula> <tex-math notation="LaTeX">$p_{X}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula> is generated and processed by conventional logic circuits. For instance, a single AND gate performs multiplication. The value <italic>X</italic> of an SN is measured by the density of 1 s in it, an information-coding scheme also found in biological neural systems. SC has uses in massively parallel systems and is very tolerant of soft errors. Its drawbacks include low accuracy, slow processing, and complex design needs. Its ability to efficiently perform tasks like communication decoding and neural network inference has rekindled interest in the field. Many challenges remain to be overcome, however, before SC becomes widespread. In this paper, we discuss the evolution of SC, mostly focusing on recent developments. We highlight the main challenges and discuss potential methods of overcoming them.
Quantitative schedulability analysis of continuous probability tasks in a hierarchical context

We define the concept of degree of schedulability to characterize the schedulability and performance of soft real-time systems. The degree of schedulability of a system is given in terms of the two factors 1) Percentage of Missed Dead- lines (PoMD); and 2) Degradation of the Quality of Service (DoQoS). Our work is set as a model-based framework for hierarchical scheduling systems where we introduce probability based sporadic tasks. The novel aspect is that we consider task arrival patterns that follow user-defined continuous probability distributions. The separately modeled task triggering events represent the system environment. We determine the degree of schedulability of a single scheduling component which can contain both periodic and sporadic tasks using statistical model checking in the form of UPPAAL SMC. Finally, we show the applicability of our framework by analyzing an avionics case study.
Growth, Migration and Causality: A Comment on Tests for Macroeconomic Feedback from Large- scale Migration Based on the Irish Experience, 1948-87

I has long been assumed that a low rate o f economic activity has been a direct cause of emigration i n the Irish context. However, pr imar i ly because no empirical research in to the reverse proposit ion has been undertaken, there is no concensus over what effect net migrat ion has had on economic activity or whether i t has had any effect at all. Theoretically, i t is not clear whether emigration has an adverse or positive impact on economic activity. I t must be determined whether those who emigrate lower or raise the average level o f product iv i ty . I f those who emigrate have product iv i ty levels below the average level (i.e., are unemployed or dependent), then i t is l ike ly that GNP per capita w i l l rise after they leave. On the other hand, i f those who emigrate have pro­ duct iv i ty levels above the average level, i t is l ikely that GNP per capita w i l l fal l . Walsh (1989) provides an impor tant empirical foundation in this regard by employing Sims' (1972) causality tests in an attempt to determine whether emigration has had any impact on Irish economic act ivi ty. Walsh finds that changes in the level of economic activity have an influence on emigration but can provide no strong evidence that emigration has any impact on GNP per capita. I n an effort to test the robustness of this result, this paper employs the Vector Autoregression ( V A R ) methodology pioneered by Sims (1980) and alternative causality tests suggested by Granger (1969).
Discriminative Map Retrieval Using View-Dependent Map Descriptor

Map retrieval, the problem of similarity search over a large collection of 2D pointset maps previously built by mobile robots, is crucial for autonomous navigation in indoor and outdoor environments. Bag-of-words (BoW) methods constitute a popular approach to map retrieval; however, these methods have extremely limited descriptive ability because they ignore the spatial layout information of the local features. The main contribution of this paper is an extension of the bag-of-words map retrieval method to enable the use of spatial information from local features. Our strategy is to explicitly model a unique viewpoint of an input local map; the pose of the local feature is defined with respect to this unique viewpoint, and can be viewed as an additional invariant feature for discriminative map retrieval. Specifically, we wish to determine a unique viewpoint that is invariant to moving objects, clutter, occlusions, and actual viewpoints. Hence, we perform scene parsing to analyze the scene structure, and consider the "center" of the scene structure to be the unique viewpoint. Our scene parsing is based on a Manhattan world grammar that imposes a quasi-Manhattan world constraint to enable the robust detection of a scene structure that is invariant to clutter and moving objects. Experimental results using the publicly available radish dataset validate the efficacy of the proposed approach.
Clustering Streaming Music via the Temporal Similarity of Timbre

We consider the problem of measuring the similarity of streaming music content and present a method for modeling, on the fly, the temporal progression of a song’s timbre. Using a minimum distance classification scheme, we give an approach to classifying streaming music sources and present performance results for auto-associative song identification and for content-based clustering of streaming music. We discuss possible extensions to the approach and possible uses for such a system.
Differentiation between Common Tick Species Using Molecular Biology Techniques in Saudi Arabia

Protein and Esterase electrophoresis were used to genetically identify two Saudi tick species. Engorged females of the camel tick Hyalomma dromedarii (Koch) (Acari: Ixodidae) and the cattle tick Boophilus annulatus (Say) (Acari: Ixodidae) ticks collected from infested camels and cattle in the animals resting house at Hail region in KSA were used. The results showed that there are a variation in both of protein and esterase activity levels and a high polymorphism within and between the genera and species of Hyalomma and Boophilus . In conclusion, the protein and esterase electrophoretic analysis used in the present study could successfully distinguish among tick species, commonly found in Saudi Arabia. Keywords—Molecular biology, The camel tick Hyalomma dromedarii, The cattle tick Boophilus annulatus , Ticks.
Model-Driven Development of a Mediation Service

Although service-oriented architectures offer real benefits when pursuing application integration and business flexibility, there are still no satisfactory solutions for dealing with existing systems that need to cooperate while their services have no perfect match. In the case of incompatible services, a 'mediator' may be introduced which resolves (semantic) interoperability problems by intervening in the cooperation between systems. Building mediators is currently often a manual process, resulting in dedicated IT-driven solutions. This paper presents a framework to guide the development of mediators, with the following objectives: (i) uncover and capture the actual interoperability problem that needs to be solved; (ii) allow the involvement of non-IT (i.e., business) experts in the development of the solution; (iii) support evolution of the solution and re-use of results in case of changing interoperability requirements; (iv) facilitate automation of parts of the process. The framework is based on service-oriented, model-driven and semantic Web techniques. Available tool support for the different steps in the framework is indicated.
Internet Economics: The Use of Shapley Value for ISP Settlement

Within the current Internet, autonomous ISPs implement bilateral agreements, with each ISP establishing agreements that suit its own local objective to maximize its profit. Peering agreements based on local views and bilateral settlements, while expedient, encourage selfish routing strategies and discriminatory interconnections. From a more global perspective, such settlements reduce aggregate profits, limit the stability of routes, and discourage potentially useful peering/connectivity arrangements, thereby unnecessarily balkanizing the Internet. We show that if the distribution of profits is enforced at a global level, then there exist profit-sharing mechanisms derived from the coalition games concept of Shapley value and its extensions that will encourage these selfish ISPs who seek to maximize their own profits to converge to a Nash equilibrium. We show that these profit-sharing schemes exhibit several fairness properties that support the argument that this distribution of profits is desirable. In addition, at the Nash equilibrium point, the routing and connecting/peering strategies maximize aggregate network profits and encourage ISP connectivity so as to limit balkanization.
Building Unified Human Descriptors For Multi-Type Activity Recognition

Activity recognition is an important as well as a difficult task in computer vision. In the past years many types of activities -- single actions, two persons interactions or ego-centric activities to name a few -- have been analyzed. Nevertheless, researchers have always treated such types of activities separately. In this paper, we propose a new problem: labeling a complex scene where activities of different types happen in sequence or concurrently. We first present a new unified descriptor, called Relation History Image (RHI), which can be extracted from all the activity types we are interested in. We then propose a new method to recognize the activities and at the same time associate them to the humans who are performing them. Next, we evaluate our approach on a newly recorded dataset which is representative of the problem we are considering. Finally, we show the efficacy of the RHI descriptor on publicly available datasets performing extensive evaluations.
Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion

Most existing low-n-rank minimization algorithms for tensor completion suffer from high computational cost due to involving multiple singular value decompositions (SVDs) at each iteration. To address this issue, we propose a novel factor matrix trace norm minimization method for tensor completion problems. Based on the CANDECOMP/PARAFAC (CP) decomposition, we first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode-n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, which leads to a convex combination problem of much smaller scale matrix nuclear norm minimization. Finally, we develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the proposed problem. Experimental results on both synthetic and real-world data validate the effectiveness of our approach. Moreover, our method is significantly faster than the state-of-the-art approaches and scales well to handle large datasets.
Anomaly detection in Smart Grid data: An experience report

In recent years, we have been witnessing profound transformation of energy distribution systems fueled by Information and Communication Technologies (ICT), towards the so called Smart Grid. However, while the Smart Grid design strategies have been studied by academia, only anecdotal guidance is provided to the industry with respect to increasing the level of grid intelligence. In this paper, we report on a successful project in assisting the industry in this way, via conducting a large anomaly-detection study on the data of one of the power distribution companies in the Czech Republic. In the study, we move away from the concept of single events identified as anomaly to the concept of collective anomaly, that is itemsets of events that may be anomalous based on their patterns of appearance. This can assist the operators of the distribution system in the transformation of their grid to a smarter grid. By analyzing Smart Meters data streams, we used frequent itemset mining and categorical clustering with clustering silhouette thresholding to detect anomalous behaviour. As the main result, we provided to stakeholders both a visual representation of the candidate anomalies and the identification of the top-10 anomalies for a subset of Smart Meters.
Foraging behavior of humpback whales: kinematic and respiratory patterns suggest a high cost for a lunge

SUMMARY Lunge feeding in rorqual whales is a drag-based feeding mechanism that is thought to entail a high energetic cost and consequently limit the maximum dive time of these extraordinarily large predators. Although the kinematics of lunge feeding in fin whales supports this hypothesis, it is unclear whether respiratory compensation occurs as a consequence of lunge-feeding activity. We used high-resolution digital tags on foraging humpback whales (Megaptera novaengliae) to determine the number of lunges executed per dive as well as respiratory frequency between dives. Data from two whales are reported, which together performed 58 foraging dives and 451 lunges. During one study, we tracked one tagged whale for approximately 2 h and examined the spatial distribution of prey using a digital echosounder. These data were integrated with the dive profile to reveal that lunges are directed toward the upper boundary of dense krill aggregations. Foraging dives were characterized by a gliding descent, up to 15 lunges at depth, and an ascent powered by steady swimming. Longer dives were required to perform more lunges at depth and these extended apneas were followed by an increase in the number of breaths taken after a dive. Maximum dive durations during foraging were approximately half of those previously reported for singing (i.e. non-feeding) humpback whales. At the highest lunge frequencies (10 to 15 lunges per dive), respiratory rate was at least threefold higher than that of singing humpback whales that underwent a similar degree of apnea. These data suggest that the high energetic cost associated with lunge feeding in blue and fin whales also occurs in intermediate sized rorquals.
Outage probability and error rates of switched diversity systems in Hoyt fading channel

Simple closed-form expressions of outage probability and moment generating function (MGF) are found for a switch and examine combiner (SEC) operating over independent and identically distributed (IID) Hoyt fading channel. Next, using the derived MGF, symbol error probability (SEP) of different M-ary modulation schemes are calculated. In addition, an analytic framework is presented for calculation of optimum switching threshold that ensures minimum outage probability or minimum SEP. The analysis is quite general in the sense that it covers switch and stay combiner (SSC) and Rayleigh fading as special cases.
Promoting learning through social networks supported by a social matching system model

Presently, a plethora of internet service offerings imply in new emerging social behaviors. Learning by following an example is a behavior already investigated in educational theories which this study applies to internet based social networks. Matching people through their common knowledge is a form of leveraging knowledge acquisition by feeding complementary information across those clusters of similar peers. This paper presents a formalized mathematical model mapping a set of learning theories to knowledge interchange inside social networks. The model identifies the best peer matching to a given knowledge among people referring to bookmarked sites. A social matching engine was implemented and used to collect evidence on learning improvements that can achieved through knowledge based peer indications.
Relay Selection in Relay-Assisted Free Space Optical Systems

We investigate transmission protocols for relay-assisted free-space optical (FSO) systems, when multiple parallel relays are employed and there is no direct link between the source and the destination. As an alternative to all-active FSO relaying, where all the available relays transmit jointly, we propose a scheme which selects only a single relay to participate in the communication between the source and the destination in each transmission slot, based on the channel state information (CSI) obtained from all FSO links. Thus, the need for synchronizing the relays' transmission is avoided, while the slowly varying nature of the atmospheric channel is exploited. For both relay selection and all-active relaying, novel analytical closed-form expressions for their outage performance are derived, assuming the Gamma-Gamma channel model. Numerical results are provided for equal and non-equal length FSO links, which clearly demonstrate the significant performance gains offered by relay selection compared to all-active relaying.
Discrimination aware classification for imbalanced datasets

The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute. Once the discrimination sensitive attribute is identified, the methods aim to develop a strategy that will include the useful information from that attribute without causing any additional discrimination. Our work focuses on an aspect often overlooked in the discrimination aware classification - the scenario of an imbalanced dataset, where the number of samples from one class is disproportionate to the other. We also investigate a strategy that is directly minimizing discrimination and is independent of the class balance. Our empirical results indicate additional concerns that need to be considered when developing discrimination aware classifiers, and our proposed strategy shows promise in overcoming these concerns.
Effects of Thyroid Hormone on Embryonic Oligodendrocyte Precursor Cell Developmentin Vivoandin Vitro


Abstract The oligodendrocyte precursor cell divides a limited number of times before terminal differentiation. The timing of differentiation depends on both intracellular mechanisms and extracellular signals, including mitogens that stimulate proliferation and signals such as thyroid hormone (TH) and retinoic acid (RA) that help trigger the cells to stop dividing and differentiate. We show here that, both in vivo and in vitro, TH is required for the normal development of rodent optic nerve oligodendrocytes, although in its absence some oligodendrocyte development still occurs, perhaps promoted by signals from axons. We also demonstrate that TH from both mother and pup plays a part in oligodendrocyte development in vivo. Finally, we show that precursors in embryonic nerve cultures differ from those in postnatal cultures in two ways: they respond much better to TH than to RA, and they respond more slowly to TH, suggesting that oligodendrocyte precursor cells mature during their early development.