Miracle-dz/UniFAR-FTUs huggingface dataset:
Dopamine D1/D5, But not D2/D3, Receptor Dependency of Synaptic Plasticity at Hippocampal Mossy Fiber Synapses that Is Enabled by Patterned Afferent Stimulation, or Spatial Learning

Although the mossy fiber (MF) synapses of the hippocampal CA3 region display quite distinct properties in terms of the molecular mechanisms that underlie synaptic plasticity, they nonetheless exhibit persistent (>24 h) synaptic plasticity that is akin to that observed at the Schaffer collateral (SCH)-CA1 and perforant path (PP)-dentate gyrus (DG) synapses of freely behaving rats. In addition, they also respond to novel spatial learning with very enduring forms of long-term potentiation (LTP) and long-term depression (LTD). These latter forms of synaptic plasticity are directly related to the learning behavior: novel exploration of generalized changes in space facilitates the expression of LTP at MF-CA3 synapses, whereas exploration of novel configurations of large environmental features facilitates the expression of LTD. In the absence of spatial novelty, synaptic plasticity is not expressed. Motivation is a potent determinant of whether learning about the spatial experience effectively occurs and the neuromodulator dopamine (DA) plays a key role in motivation-based learning. Prior research on the regulation by DA receptors of long-term synaptic plasticity in CA1 and DG synapses in vivo suggests that whereas D2/D3 receptors may modulate a general predisposition toward expressing plasticity, D1/D5 receptors may directly regulate the direction of change in synaptic strength that occurs during learning. Although the CA3 region is believed to play a pivotal role in many forms of learning, the role of dopamine receptors in persistent (>24 h) forms of synaptic plasticity at MF-CA3 synapses is unknown. Here, we report that whereas pharmacological antagonism of D2/D3 receptors had no impact on LTP or LTD, antagonism of D1/D5 receptors significantly impaired LTP and LTD that were induced by solely by means of patterned afferent stimulation, or LTP/LTD that are typically enhanced by the conjunction of afferent stimulation and novel spatial learning. These data indicate an important role for DA acting on D1/D5 receptors in the support of long-lasting and learning-related forms of synaptic plasticity at MF-CA3 synapses and provide further evidence for an important neuromodulatory role for this receptor in experience-dependent synaptic encoding in the hippocampal subfields.
Conflict and Performance in Global Virtual Teams

Increasing globalization and advances in communication technology have fuelled the emergence of global virtual teams (GVTs). There is much potential for conflict in GVTs as members work across cultural, geographical, and time boundaries. This study examines the antecedents of GVT conflict and the circumstances under which conflict affects team performance. An in-depth study of GVT conflict episodes was carried out using interviews, observations, communication logs, and documents. Based on findings from the teams under study interpreted in the light of prior literature, propositions are developed about the antecedents and effects of GVT conflict as stated. Within GVTs, cultural diversity is likely to contribute to both task and relationship conflict while functional diversity may result in task conflict. Large volumes of electronic communication and lack of immediacy of feedback in asynchronous media can contribute to task conflict. Moreover, the relationship between task conflict and team performance is likely to be contingent upon task complexity and conflict resolution approach. The influence of relationship conflict on performance may depend on task interdependence and conflict resolution approach. The conflict resolution approach may in turn be determined by the nature of conflict attribution. These propositions have been synthesized into a model to guide future empirical research and GVT practice.
Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.
Neuro-Fuzzy Approaches to Decision Making: Application to Check Authorization from Incomplete Information

We describe the application of a multilayer perceptron, polynomial neural networks, and a fuzzy decision model to the problem of check approval from incomplete data. A simple benchmark case was established as a performance metric to compare the various non-linear strategies that were implemented. An overall improvement of at least 10% was obtained in each of these methods.
Evaluating Reliability-Testing Usage Models

Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. Markov usage models and their variations have been found to be well suited for generating test cases that are statistically close to what the application is expected to receive when in production. In this article, we study the specific case of web applications. We present an evaluation method for estimating the accuracy of various reliability-testing usage models. The method is based on comparison between observed users' traces and traces inferred from the usage model. Our method gauges the accuracy of the reliability-testing usage model by calculating the sum of goodness-of-fit values of each traces and scaling the result between 0 and 1.
Notice of RetractionA Study on the Improvement of Human Capital Measurement and Management with Activity Based Costing

The measurement and management of human capital is a problem which has puzzled economists for a long time. Until now, there isn't an effective way to calculate the cost of human capital accurately. In recent years, activity based costing (ABC) has been well developed and applied in many industries in western countries. It is an advanced method of cost calculation and may provide us a useful tool to measure human capital. This paper analyzed the cost characteristics of human capital with the theory of ABC. Key activities in the process of human capital's development were defined. Resource costs were allocated accurately and the procedure of human capital's calculation was proposed. It provides us a new idea to measure and manage human capital. It is different from the traditional methods and will do some help in further studies.
The apnea-ECG database

Sleep apnea is a sleep disorder with a high prevalence in the adult male population. Sleep apnea is regarded as an independent risk factor for cardiovascular sequelae such as ischemic heart attacks and stroke. The diagnosis of sleep apnea requires polysomnographic studies in sleep laboratories with expensive equipment and attending personnel. Sleep apnea can be treated effectively using nasal ventilation therapy (nCPAP). Early recognition and selection of patients with sleep related breathing disorders is an important task. Although it has been suggested that this can be done on the basis of the ECG, careful quantitative studies of the accuracy of such techniques are needed. An annotated database with 70 nighttime ECG recordings has been created to support such studies. The annotations were based on visual scoring of disordered breathing during sleep.
SPATIAL HIERARCHICAL BAYESIAN MODEL FOR MISSING DATA IMPUTATION [ early draft ]

The application of a hedonic approach to real estate data has played an important role in property market analysis. Although the recent diffusion of global positioning systems (GPS), geographic information systems (GIS), and remote sensing (RS) have provided access to detailed attributes of properties, missing data continues to be a serious issue while conducting a hedonic analysis of properties. Knight et al. (1998) had proposed a method for imputing missing data effectively by employing the Markov Chain Monte Carlo (MCMC) method. However, the work of Knight et al. (1998) did not deal with spatial effects among data, which are now widely considered to be rather important aspects of real estate data. Therefore, the objective of this study is to extend the work of Knight et al. (1998) by considering spatial dependence. In order to consider spatial dependence, this study adopts spatial statistical instead of spatial econometric models. This is because spatial statistical models are more convenient for conducting out-of-sample prediction. We formulate the hedonic regression model as the hierarchal Bayesian model by considering spatial dependence (spatial hierarchal Bayesian model), and append the data imputation steps to the MCMC simulation in keeping with the method proposed by Knight et al. (1998). In addition, this study conducts an empirical analysis of the Japanese apartment rent market, and demonstrates out-of-sample predictions. As compared to Knight et al.’s (1998) model, the empirical results indicate a higher predictive accuracy using our model.
Implementing Explicit Congestion Notification (ECN) in TCP for IPv6

This paper discusses our design and implementation of Explicit Congestion Notification (ECN) for TCP on IPv6. The first part of the paper describes how ECN marks packets instead of dropping them to signal incipient congestion. Marking packets instead of dropping them carries a number of benefits, such as avoiding costly delays waiting for retransmissions timeouts or dropping packets on flows sensitive to even a single packet loss. The next part of the paper then reviews what changes are required to routers and to hosts to implement an ECN scheme. Our specific implementation is described, including justifications for some of our design decisions. We then explain how we tested our implementation, evaluating both only the router’s behavior and the end-to-end performance of our system. Finally, we analyze our testing results and offer conclusions and other lessons learned from our research.
An Experiment About Using Copulative and Comparative Sentences as Constraining Relations

Existing search engines and question-answering (QA) systems have made possible processing large volumes of textual information. Current work on QA has mainly focused on answering two basic types of questions: factoid and definition questions. However, the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base is not among the capabilities of those systems. In this paper, a system oriented to infer query answers from a collection of propositions expressed in natural language is introduced. By means of a specific example, it is outlined how the system proceeds to face those situations. This approach is based on the use of formal constraining relations modeling copulative and comparative sentences. Combining those propositions with others contained in different knowledge bases and applying deduction rules, the desired answer could be obtained.
Research of Distributed Intrusion Detection System Model Based on Mobile Agent

Itrusion detection has become an important approach to solve the security problems. On the basis of analyzing the defects of the modern intrusion detection system and migrating with the technology of mobile agent, this article proposes a distributed intrusion detection system model based on agents. The function of each module in the system and the advantage of the whole system are described in detail. The proposed model adopts the architecture which has no control server and realizes the distributed detection and reaction really. Each component in the system independent and executes its function. The model is an open system with good flexibility and expansibility, and the problem of a single point failure is solved completely. Finally, the implementation, evaluation and future works are presented.
The basic competitiveness factors shaping the innovation performance of countries

The goal of this research is to use Bayesian Networks to discover the relations among the components of competitiveness and the innovation level of countries.For this purpose, initially the competitiveness performance of 148 countries is analyzed using an integrated cluster analysis and factor analysis framework. This facilitates the basic areas where each cluster group demonstrates a good performance and those where they need improvements relative to the othergroups in order to increase their competition level. Subsequently; a Bayesian Network is constructed using WinMine software based on competitiveness indicators drawn from WEF pillars and sub-pillars. This analysis, in its turn,investigates whether the competitiveness stage to which a country belongs has an important impact on its innovation performance and highlights, which of the basic competitiveness variables has a significant impact in shaping its innovation level.
Gate-diffusion input (GDI): a power-efficient method for digital combinatorial circuits

Gate diffusion input (GDI) - a new technique of low-power digital combinatorial circuit design - is described. This technique allows reducing power consumption, propagation delay, and area of digital circuits while maintaining low complexity of logic design. Performance comparison with traditional CMOS and various pass-transistor logic design techniques is presented. The different methods are compared with respect to the layout area, number of devices, delay, and power dissipation. Issues like technology compatibility, top-down design, and precomputing synthesis are discussed, showing advantages and drawbacks of GDI compared to other methods. Several logic circuits have been implemented in various design styles. Their properties are discussed, simulation results are reported, and measurements of a test chip are presented.
A wearable system that knows who wears it

Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions. Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b)~the device is physically on the wearer's body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day's worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus~S smart phone and custom-designed module for the Shimmer sensing platform.
A COMPARISON OF CHLOROPLAST MEMBRANE SURFACES VISUALIZED BY FREEZE-ETCH AND NEGATIVE STAINING TECHNIQUES; AND ULTRASTRUCTURAL CHARACTERIZATION OF MEMBRANE FRACTIONS OBTAINED FROM DIGITONIN-TREATED SPINACH CHLOROPLASTS

Spinach chloroplast lamellae were washed free of negatively staining surface particles (carboxydismutase and coupling factor protein) and the resulting smooth-surfaced lamellae still showed the usual large (175 A) and small (110 A) particles seen by freeze-etching. Therefore, the freeze-fracture plane probably occurs along an internal surface of the chloroplast membrane. Fractions obtained by differential centrifugation of digitonin-treated chloroplast membranes were studied by negative staining, thin sectioning, and freeze-etching techniques for electron microscopy. The material sedimenting between 1,000 g and 10,000 g, enriched in photosystem II activity, was shown to consist of membrane fragments. These freeze-etched membrane fragments were found to have large particles on most of the exposed fracture faces. The large particles had the same size and distribution pattern as the 175 A particles seen in intact chloroplast membranes. The material sedimenting between 50,000 g and 144,000 g, which had only photosystem I activity, was found to consist of particles in various degrees of aggregation. Freeze-etching of this fraction revealed only small particles corresponding to the 110 A particles seen in intact chloroplasts. A model is presented suggesting that chloroplast lamellar membranes have a binary structure, which digitonin splits into two components. The two membrane fragments have different structures, revealed by freeze-etching, and different photochemical and biochemical functions.
Domain Adaptation for Upper Body Pose Tracking in Signed TV Broadcasts

The objective of this work is to estimate upper body pose for signers in TV broadcasts. Given suitable training data, the pose is estimated using a random forest body joint detector. However, obtaining such training data can be costly. The novelty of this paper is a method of transfer learning which is able to harness existing training data and use it for new domains. Our contributions are: (i) a method for adapting existing training data to generate new training data by synthesis for signers with different appearances, and (ii) a method for personalising training data. As a case study we show how the appearance of the arms for different clothing, specifically short and long sleeved clothes, can be modelled to obtain person-specific trackers. We demonstrate that the transfer learning and person specific trackers significantly improve pose estimation performance.
Fostering Entrepreneurial Learning On‐the‐Job: evidence from innovative small and medium‐sized companies in Europe

As economies become more innovation-driven, the need for entrepreneurial behaviour amongst employees working for existing companies increases in order to enhance the organisations' capacity to develop new ideas, products and services. Hence, entrepreneurial learning and the development of entrepreneurial competencies of employees on-the-job become more important. One of the most crucial competencies in this regard is the ability to identify potential business opportunities, referred to as opportunity identification competence (OIC). In this empirical study, antecedents of OIC were investigated in a small and medium-sized business context. Based on the 3-P (i.e. presage, process, product) model, specific learner, work environment, and process factors influencing OIC as an outcome variable were studied. More than 200 employees from 12 companies completed a questionnaire. Results of a backward regression analysis underline the importance of investing in programmes that focus on entrepreneurial learning at the shop floor level, trusting employees that they are capable of actively participating in the early stages of innovation and the crucial role of owner-managers to support entrepreneurial employee activities.
Practical Algorithms For Online Routing on SIMD Meshes

The problem of online routing on SIMD meshes arises when these processors are used for real-time analysis of sensory input. Existing algorithms--ones not making unrealistic hardware assumptions--use sorting as a subroutine and so, while asymptotically optimal, have running times at least 3 times greater (for likely array sizes) than the lower bound of 4n-4 communication steps. Dedicated router networks address this problem, but are costly and not available on all processors. We present a greedy algorithm with very low overhead based on wormhole routing, which can be modified to use broadcast buses and reconfigurable broadcast buses to successively improve performance. While we prove a bad worst case, performance is usually at worst a small additive constant from optimal, and always within a factor of 2 for every communication pattern tried. We also show that preprocessing randomization can improve the realiability of this result. Other algorithms are presented: one takes advantage of communication where all packets must travel only a short distance; another uses broadcast buses to transmit packets, improving performance when communication is sparse. Finally, we present performance results of these algorithms with respect to increase path width and packet splitting.
LightNVM: The Linux Open-Channel SSD Subsystem

As Solid-State Drives (SSDs) become commonplace in data-centers and storage arrays, there is a growing demand for predictable latency. Traditional SSDs, serving block I/Os, fail to meet this demand. They offer a high-level of abstraction at the cost of unpredictable performance and suboptimal resource utilization. We propose that SSD management trade-offs should be handled through Open-Channel SSDs, a new class of SSDs, that give hosts control over their internals. We present our experience building LightNVM, the Linux Open-Channel SSD subsystem. We introduce a new Physical Page Address I/O interface that exposes SSD parallelism and storage media characteristics. LightNVM integrates into traditional storage stacks, while also enabling storage engines to take advantage of the new I/O interface. Our experimental results demonstrate that LightNVM has modest host overhead, that it can be tuned to limit read latency variability and that it can be customized to achieve predictable I/O latencies.
¿Por Favor? Favor Reciprocation When Agents Have Private Discounting

When agents can significantly increase other agents’ utility at a moderate cost, the socially optimal outcome is for all agents to repeatedly provide favors to each other whenever they can. However, when agents cannot support or enforce a market system, this forms a situation similar to the repeated prisoner’s dilemma because each agent can unilaterally improve its own utility by refusing to help others. We present an adaptive tit-for-tat strategy that provides a mutually beneficial equilibrium in the general cases when agents may have differing private discount factors and when favor costs and benefits are stochastic and asymmetric. This strategy allows agents to treat previously unencountered agents with caution, communicate about the trustworthiness of other agents, and evaluate past communication for deception. We discuss the details of the strategy, analytic and simulation results, and the impact of various parameterizations. We analyze one form of communication in detail and find that it causes agents to be more protective of utility.