Miracle-dz/UniFAR-FTUs huggingface dataset:
Expectations and Two-Sided Platform Profits ∗

We study the effect of different types of rational expectations on two-sided platforms equilibrium profits—under monopoly and competition. One side (developers) always forms rational and responsive expectations. In contrast, we distinguish several ways in which the other side (users) may form rational expectations. Two extremes are responsive expectations (expectations that adjust perfectly to price changes on both sides) and passive expectations (users do not observe prices charged to developers and have fixed expectations about the number of developers who join). We also consider expectation formation mechanisms in-between the extremes: a hybrid of passive and responsive expectations, and wary expectations (users do not observe prices charged to developers but assume they are set optimally by platforms, given the user prices observed). We show that platforms with market power (monopoly) prefer more responsive over less responsive expectations. In contrast, platforms in strongly competitive environments have the opposite preference: they derive higher profits when users have less responsive expectations. The main reason is that more responsive expectations amplify the effect of price reductions. Platforms with market power benefit because this leads to demand increases, which they are able to capture fully. Competing platforms suffer because more responsive expectations intensify price competition.
Applying ATAM to evaluate indoor location systems for smartphones

Indoor Location has been studied for some time and has become a very popular topic recently. Although many techniques for indoor positioning exist, the right choice depends on the context. The context is composed of many factors, for example, the service provided, cost, maintenance, etc. Moreover, new techniques for indoor positioning are still being developed. This paper applies the Architecture Trade-off Analysis Method (ATAM) in a joint scenario for indoor location, presenting how architectural decisions influence the quality attributes for the system. Finally, the paper proposes a generic object-oriented model for indoor location, making it trivial to exchange between techniques, algorithms and sensors within a mobile application during the localization process.
Tardive dyskinetic syndrome in rats infected with Borna disease virus

Tardive Dyskinesia (TD) is a hyperkinetic movement disorder caused by chronic treatment of psychiatric patients with dopamine (DA) receptor blocking drugs (Stacy & Jankovic 1991). Although TD is one of the most important and frequently encountered iatrogenic disorders in clinical medicine, its pathophysiology is poorly understood. We have observed a hyperkinetic movement disorder in rats experimentally infected with a neurotropic RNA virus, Borna disease virus, that may provide important insights into the pathophysiology of TD. Like TD patients, infected rats show prominent orofacial dyskinesias. In keeping with the dopamine (Goetz & Klawans 1982) and anatomic (Fibiger & Lloyd 1984) hypotheses of TD, the Borna disease rat model shows enhanced behavioural sensitivity to DA agonists and selective striatal cell damage. There is also evidence of DA deafferentation and heterogeneous reduction of D2 binding in the caudate-putamen, particularly from sites implicated in oral behaviour. These observations on a virus-induced movement disorder offer novel approaches to TD pathogenesis.
Analytically inducting option cash flows for Markovian interest rate models: A new application paradigm

This paper develops a new computational approach for general multi- factor Markovian interest rate models. The early exercise premium is derived for general American options. The option cash flows are decomposed into fast and slowly varying components. The fast components are option independent and derived analytically. The slow components are calculated by controlled expansion for finite time intervals. The option price is obtained by iterating the analytic expressions of one time interval. For one-factor models, the critical boundary for American options has a universal form near maturity. For American put stock options, analytic expressions are derived to approximate the critical boundary. The put price calculated from the boundary has relative precision better than $10^{-5}$ in all cases.
When opposites lead to the same: a direct comparison of explicit and implicit disgust regulation via fMRI

Abstract Cognitive reappraisal and placebo administration constitute two different approaches for modulating one’s own emotional state. Whereas reappraisal is an explicit (effortful) type of self-regulation, placebo treatment initiates implicit processes of affective control. The brain mechanisms underlying these processes have not been directly compared with each other up until now; doing this enables the identification of distinct and shared neuronal features. We conducted a functional magnetic resonance imaging study with 45 women, who were presented with disgusting and neutral images in a block design, at three experimental sessions, over 3 consecutive days. They were asked to passively view the images in one session, engage in reappraisal in another, and in another session they received a placebo pill: a disgust-reducing ‘anti-nausea drug’. Relative to passive viewing, both reappraisal and placebo treatment effectively reduced the experienced disgust intensity. In the placebo condition, this reduction was associated with decreased activation of the insula and the dorsolateral prefrontal cortex (DLPFC). In contrast, reappraisal induced increased activation in both regions. Furthermore, both regulation strategies were associated with opposite patterns of connectivity in a network encompassing the amygdala, the insula and the DLPFC. Only placebo administration led to a reduced coupling in this network.
An information exploration tool for performance analysis of Java programs

The diagnosis of performance and memory problems can require the analysis of large and complex data sets describing a program's execution. An analysis tool must help the user both find the right organization of the data to uncover useful information, and work with the data through a lengthy and unpredictable discovery process. We present Jinsight EX, a tool for analyzing Java performance, that adopts techniques that have been successfully used to explore large data sets in other application domains, and adapts them specifically to the needs of program execution analysis. We introduce execution slices, a high-level organizing abstraction that the user may define and then easily reuse in various settings. We illustrate techniques that allow the user to perform a range of common analysis tasks and to structure a longer analysis process, using this abstraction. We present the tool, its implementation and initial experience of its use.
Ranking Documents Through Stochastic Sampling on Bayesian Network-based Models: A Pilot Study

Using approximate inference techniques, we investigate in this paper the applicability of Bayesian Networks to the problem of ranking a large set of documents. Topology of the network is a bipartite. Network parameters (conditional probability distributions) are determined through an adoption of the weighting scheme tf-idf. Rank of a document with respect to a given query is defined as the corresponding posterior probability, which is estimated through performing Rejection Sampling. Experimental results suggest that performance of the model is at least comparable to the baseline ones such as BM25. The framework of this model potentially offers new and novel ways in weighting documents. Integrating the model with other ranking algorithms, meanwhile, is expected to bring in performance improvement in document ranking.
Statistical humerus atlas for optimal design of Asian-specific humerus implants and associated intramedullary robotics

The statistical shape analysis of population-specific anatomical structures plays a pivotal role in both implant design and associated dedicated surgical robotic instruments. The sizes of current off-the-shelf humerus implants are unable to accommodate Asian patients since they are mainly produced for the American and European countries based on locally collected data. In this paper, an algorithm to construct a statistical humerus model is proposed so as to create a reference model for the development of humerus implants that conforms to the Asian population, and for the related design of snake-like robot for intramedullary procedures. The statistical humerus model is constructed from 14 CT datasets of male patients by implementing a three-step simplified method with improved alignment process. We have further constructed the statistical atlas showing the internal anatomy of humerus by adopting a spherical harmonics based method for the aim of developing intramedullary based implants used for humerus fixation. The validation results demonstrate that the proposed method significantly improves the efficiency of alignment without compromising the accuracy of the atlas. A comparison between the atlas constructed using the proposed method and that constructed utilizing the framework of spherical harmonics is further illustrated.
PEDF Regulates Vascular Permeability by a γ-Secretase-Mediated Pathway

Increased vascular permeability is an inciting event in many vascular complications including diabetic retinopathy. We have previously reported that pigment epithelium-derived factor (PEDF) is able to inhibit vascular endothelial growth factor (VEGF)-induced angiogenesis through a novel γ-secretase-dependent pathway. In this study, we asked whether inhibition of VEGF-induced permeability by PEDF is also γ-secretase-mediated and to dissect the potential mechanisms involved. Vascular permeability was assessed in vitro by measuring transendothelial resistance and paracellular permeability to dextran and in vivo by following leakage of intravenous FITC-labelled albumin into the retina in the presence or absence of VEGF and PEDF in varying combinations. Experiments were undertaken in the presence or absence of a γ-secretase inhibitor. To assess junctional integrity immunohistochemistry for the adherens junction (AJ) proteins, VE-cadherin and β-catenin, and the tight junction (TJ) protein, claudin-5 was undertaken using cultured cells and flat mount retinas. Protein expression and the association between AJ proteins, VEGF receptors (VEGFRs) and γ-secretase constituents were determined by immunoprecipitation and Western Blot analysis. In selected experiments the effect of hypoxia on junctional integrity was also assessed. PEDF inhibition of VEGF-induced permeability, both in cultured microvascular endothelial cell monolayers and in vivo in the mouse retinal vasculature, is mediated by γ-secretase. PEDF acted by a) preventing dissociation of AJ and TJ proteins and b) regulating both the association of VEGF receptors with AJ proteins and the subsequent phosphorylation of the AJ proteins, VE-cadherin and β-catenin. Association of γ-secretase with AJ proteins appears to be critical in the regulation of vascular permeability. Although hypoxia increased VEGFR expression there was a significant dissociation of VEGFR from AJ proteins. In conclusion, PEDF regulates VEGF-induced vascular permeability via a novel γ-secretase dependent pathway and targeting downstream effectors of PEDF action may represent a promising therapeutic strategy for preventing or ameliorating increased vascular permeability.
Self managing monitoring for highly elastic large scale cloud deployments

Infrastructure as a Service computing exhibits a number of properties, which are not found in conventional server deployments. Elasticity is among the most significant of these properties which has wide reaching implications for applications deployed in cloud hosted VMs. Among the applications affected by elasticity is monitoring. In this paper we investigate the challenges of monitoring large cloud deployments and how these challenges differ from previous monitoring problems. In order to meet these unique challenges we propose Varanus, a highly scalable monitoring tool resistant to the effects of rapid elasticity. This tool breaks with many of the conventions of previous monitoring systems and leverages a multi-tier P2P architecture in order to achieve in situ monitoring without the need for dedicated monitoring infrastructure. We then evaluate Varanus against current monitoring architectures. We find that conventional monitoring tools perform acceptably for small, non changing cloud deployments. However in the case of large or highly elastic deployments current tools perform unacceptably incurring increased latencies, high load and slowed operation necessitating that a new, alternative tool be used. Further, we demonstrate that Varanus maintains low latency and low resource monitoring state propagation at scale and during during periods of high elasticity.
Improving program comprehension by automatic metamodel abstraction

The maintenance of a software system represents an important part in its lifetime. In general, each software system is the subject of different kinds of changes. Bug fixes and a new functionality extensions are the most common reasons for a change. Usually, a change is accomplished by source code modifications. To make such a modification, correct understanding the current state of a system is required. This paper presents the innovative approach to the simplification of program comprehension. Based on the presented method, the affected software system is analyzed and metamodel for the selected feature is created. The feature represents functional aspect of a system being the subject of the analysis and change. The main benefit is that by focusing on well known (and precisely described) parts of program implementation, it is possible to create metamodel for implementation parts automatically. The level of metamodel is at a higher level of abstraction than implementation.
Production of Chitin and Chitosan from Partially Autolyzed Shrimp Shell Materials

The effect of partial autolysis during storage of shrimp biowaste on the quality of the extracted chitin and chitosan has been investigated. Shrimp waste materials were stored in a 30 o C incubator for 1-4 days. The quality of the chitin and chitosan extracted from these materials was assessed by ash and protein analysis and in solution by viscosimetry. Chitosan from fresh biowaste, treated for 1 day at 30 o C showed the highest viscosity 3590 cps. Chitosan from frozen material showed the highest viscosity at 3580 cps after 2 days at 30 o C. The corresponding values for chitosan without 30 o C treatment were 2300 and 2700 cps. Normally, viscosity, solubility and molecular weight of chitosan are closely related. The data of this study show that after partial autolysis the viscosity of the chitosan obtained has increased, but that other characteristics of chitosan such as degree of deacetylation, molecular weight, solubility and turbidity did not change.
Multithreaded Code from Synchronous Programs: Generating Software Pipelines for OpenMP

In this paper, we describe an automatic synthesis procedure that translates synchronous programs to software pipelines, i.e. to multithreaded software systems whose sequential threads form single stages of a pipeline which are connected by FIFO buffers. The single-loop form that is required for pipelining is thereby already given by a pre-processing step that translates the synchronous programs to synchronous guarded actions. These synchronous guarded actions are analyzed in terms of their data-dependencies to define legal partitions into pipeline stages. Given such a legal partitioning into pipeline stages, the presented synthesis procedure automatically identifies potential pipeline conflicts and implements code for forwarding (if possible) while stalling is implicitly given by the FIFO buffers. Finally, the sequential threads for the conflict-free pipeline stages are implemented in OpenMP-based C-code. We demonstrate the usefulness of our approach by some preliminary experimental results.
Variance Based Measure for Optimization of Parametric Realignment Algorithms

Neuronal responses to sensory stimuli or neuronal responses related to behaviour are often extracted by averaging neuronal activity over large number of experimental trials. Such trial-averaging is carried out to reduce noise and to diminish the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded neuronal responses are jittered in time with respect to the corresponding stimulus or behaviour, averaging over trials may distort the estimation of the underlying neuronal response. Temporal jitter between single trial neural responses can be partially or completely removed using realignment algorithms. Here, we present a measure, named difference of time-averaged variance (dTAV), which can be used to evaluate the performance of a realignment algorithm without knowing the internal triggers of neural responses. Using simulated data, we show that using dTAV to optimize the parameter values for an established parametric realignment algorithm improved its efficacy and, therefore, reduced the jitter of neuronal responses. By removing the jitter more effectively and, therefore, enabling more accurate estimation of neuronal responses, dTAV can improve analysis and interpretation of the neural responses.
Our troubles with Linux Kernel upgrades and why you should care

Linux and other open-source Unix variants (and their distributors) provide researchers with full-fledged operating systems that are widely used. However, due to their complexity and rapid development, care should be exercised when using these operating systems for performance experiments, especially in systems research. In particular, the size and continual evolution of the Linux code-base makes it difficult to understand, and as a result, decipher and explain the reasons for performance improvements. In addition, the rapid kernel development cycle means that experimental results can be viewed as out of date, or meaningless, very quickly. We demonstrate that this viewpoint is incorrect because kernel changes can and have introduced both bugs and performance degradations. This paper describes some of our experiences using Linux and FreeBSD as platforms for conducting performance evaluations and some performance regressions we have found. Our results show, these performance regressions can be serious (e.g., repeating identical experiments results in large variability in results) and long lived despite having a large negative effect on performance (one problem was present for more than 3 years). Based on these experiences, we argue: it is sometimes reasonable to use an older kernel version, experimental results need careful analysis to explain why a performance effect occurs, and publishing papers validating prior research is essential.
A survey on digital background calibration of ADCs

In this paper, a general description of digital ADC calibration approaches in current state-of-the-art is presented, with particular emphasis in Pipeline converters. The study performs a classification of the existing techniques considering two basic aspects: a) the principle of operation and the particular errors which can be compensated after calibration, b) the process from which a measurement of the errors, and therefore the calibrated output code, is obtained. Attention will be paid to those approaches applied in background mode and hence not requiring the interruption of the normal ADC operation.
RM-SORN: a reward-modulated self-organizing recurrent neural network

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.
Non-Systematic Backtracking for Mixed Integer Programs

Av ariety of hybrids of Constraint Programming, Artificial Intelligence and Operations Research techniques have given impressive results. Three recent approaches are (i) the use of relaxations in constraint systems, (ii) non-systematic backtracking to boost the scalability of constraint solvers, and (iii) non-systematic backtracking to boost the scalability of branch-and-bound search. This paper describes a hybrid of all three approaches that combines non-systematic backtracking, SAT-based inference and linear relaxation. It is intended for large MIPs that are hard for reasons of both optimisation and feasibility. Such problems are of practical as well as theoretical interest and we expect the hybrid to find many applications. It is currently under development and results will be reported at the workshop.
Cosmological constant and curved 5D geometry

We study the value of cosmological constant in de Sitter brane embedded in five dimensions with positive, vanishing and negative bulk cosmological constant. In the case of negative bulk cosmological constant, we show that not zero but tiny four-dimensional cosmological constant can be realized by tiny deviation from bulk curvature of the Randall-Sundrum model.
Web-Marketing in Social Networks Using MDA Approach

the evolution of the Web-marketing meaning the passage of the Webmarketing 2.0 to 3.0, baptized " Semantic Web-marketing ", the main idea being to make easier to the Internet user to use a huge and disrupting stream of information and make it organized and easily accessible. As so, we have chosen to use the N-tiers applications and the MDA (Model Driving Architecture) transformation. In this paper we are going to present a model-driven approach to the development of N-tiers web applications based on the UML class diagram. The transformation language is the MOF 2.0 QVT (Meta-Object Facility 2.0 Query-ViewTransformation) standard which defines the meta-model for the development of model transformation. The transformation rules defined in this paper can generate, from the class diagram, an XML file containing the layers of N-tiers web application respecting a MVC2 (Model-View-Controller), DI (Dependency Injection) and DAO (Data Access Object) patterns. This file can be used to generate the end-toend necessary code of a web application
Query Processing Based on Associated Semantic Context Inference

Context-based query processing methods are used to capture user intents behind query inputs. General context models are not flexible or explicable enough for inference, because they are either static or implicit. This paper improves current context model and proposes a novel query processing approach based on associated semantic context inference. In our approach, the formal defined context is explicit, which is convenient to explore potential information during query processing. Furthermore, the context is dynamically constructed and further modified according to specific query tasks, which ensures the precision of context inference. For given query inputs, the approach builds concrete context models and refines queries based on semantic context inference. Finally, queries are translated into SPARQL for query engine. The experiment shows that the proposed approach can further improve query intents understanding to guarantee precision and recall in retrieval.