Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Caching of Web

With the increasing popularity of the World Wide Web, the amount of information available and the use of Web servers are growing exponentially. As a consequence, the number of requests to popular Web servers increases exponentially as well. In order to reduce the overhead induced by frequent requests to the same documents, server caching, also referred to as main memory caching, has been proposed and implemented. In this work, we propose a static caching mechanism which consists in updating the contents of the cache periodically and, at the update time, brings in the cache only the most requested documents in the previous time interval. This caching policy has a lower management overhead. Under some statistical assumptions we show that static caching has the highest hit rate. We also provide empirical comparison results obtained by trace-driven simulations. It turns out that static caching is more eecient in terms of hit rate than those analyzed in the literature.
The Influencing Factors for Purchasing Intentions in Social Media by Utaut Perspective

The purpose of this chapter is to predict the factors influencing consumer intention towards the adoption of social commerce. This study uses a survey approach with reference to important behavioural factors such as performance expectation, social influence, price value, habits, and perceived usefulness. The results of the study revealed that habits and social influence has an important role of individuals' behavioral intention to use social commerce. A survey was administered and a sample of 250 individuals were analyzed using SmartPLS version 3.2. 7.
Stroscope: Multi-Scale Visualization of Irregularly Measured Time-Series Data

For irregularly measured time-series data, the measurement frequency or interval is as crucial information as measurements are. A well-known time-series visualization such as the line graph is good at showing an overall temporal pattern of change; however, it is not so effective in revealing the measurement frequency/interval while likely giving illusory confidence in values between measurements. In contrast, the bar graph is more effective in showing the frequency/interval, but less effective in showing an overall pattern than the line graph. We integrate the line graph and bar graph in a unified visualization model, called a ripple graph, to take the benefits of both of them with enhanced graphical integrity. Based on the ripple graph, we implemented an interactive time-series data visualization tool, called Stroscope, which facilitates multi-scale visualizations by providing users with a graphical widget to interactively control the integrated visualization model. We evaluated the visualization model (i.e., the ripple graph) through a controlled user study and Stroscope through long-term case studies with neurologists exploring large blood pressure measurement data of stroke patients. Results from our evaluations demonstrate that the ripple graph outperforms existing time-series visualizations, and that Stroscope has the efficacy and potential as an effective visual analysis tool for (irregularly) measured time-series data.
When Punishment Strikes Late: The Effect of a Delay in Punishment and Punishment Feedback on Cooperation and Efficiency

Laboratory experiments established that peer punishment is a powerful cooperation-enhancing institution. However, this evidence centers on punishment that affects punishees immediately, whereas in many out-of-lab instances, punishment only takes effect some time after it has been inflicted. Moreover, studying the consequences of a delay in the effect of punishment could shed light on the channels through which punishment facilitates cooperation. A delay may (a) prevent punishees from immediately responding to the punishment and (b) dissolve the perceived link between received punishment and past behavior. In the present study, we model a situation where punishment affects the punishee 5 periods after it has been inflicted. We find that even under a delay in the effect and feedback of punishment, peer punishment is very effective in facilitating cooperation. However, peer punishment is only efficient when a salient link between received punishment and past contributions is established.
Predicting financial distress: The role of earnings quality

This study investigates the role of earnings quality (EQ) in the prediction of financial distress. Specifically, I predict and find that EQ is positively associated with the informativeness of both accounting- and price-based distress predictors, and negatively associated with distress risk, itself. These results hold across several EQ measures and various distress prediction models, and are driven by both components of EQ measures - that related to firm fundamentals and that related to managerial discretion. Furthermore, I find that incorporating the impact of EQ improves prediction models' out-of-sample performance, especially when the forecast horizon is longer than one year. These results contribute to the literature by documenting that EQ impacts the prediction of financial distress, the most crucial input in the lending process.
Unsupervised Learning of Multi-Object Event Classes ∗

We present a novel approach for automatically inferring models of multiobject events. Objects are first detected and tracked, their motion is then segmented into a set of primitive events. These primitive events then form the nodes in a Markov network that encodes the entire event space. A bottomup/top-down search algorithm is developed to detect typical event structures that are used for classifying an observed multi-object event. We demonstrate our algorithm on clustering and inferring events in a table-laying scene.
Fourier spectral simulations for wake fields in conducting cavities

We investigate Fourier spectral time-domain simulations applied to wake field calculations in two-dimensional cylindrical structures. The scheme involves second-order explicit leap-frogging in time and Fourier spectral approximation in space, which is obtained from simply replacing the spatial differentiation operator of the YEE scheme by the Fourier differentiation operator on nonstaggered grids. This is a first step toward investigating high-order computational techniques with the Fourier spectral method, which is relatively simple to implement.
BRUNO: A high performance traffic generator for network processor

The current software tools for traffic generation suffer from poor performance in terms of frames per second and timing/rate accuracy, because of the intrinsic limitations of the PC architecture. This paper proposes a different approach, based on a cooperative PC/NP architecture: an advanced software tool runs on a host PC and instructs the processing engines of an Intel IXP2400 network processor, which take care of the actual traffic generation. This way, we keep the high flexibility of PC solutions while outperforming them in terms of packet rate. In addition, a time correction mechanism is introduced, in order to improve the system precision in traffic models reproduction. The adoption of such a mechanism is justified through statistical analysis of the overall system: the experiments carried out prove the effectiveness of this approach in reducing the mean interdeparture time error. Moreover, they show the capability of BRUNO in generating 1.5 Mpps with high accuracy.
Black hole microstates and attractor without supersymmetry

Due to the attractor mechanism, the entropy of an extremal black hole does not vary continuously as we vary the asymptotic values of various moduli fields. Using this fact we argue that the entropy of an extremal black hole in string theory, calculated for a range of values of the asymptotic moduli for which the microscopic theory is strongly coupled, should match the statistical entropy of the same system calculated for a range of values of the asymptotic moduli for which the microscopic theory is weakly coupled. This argument does not rely on supersymmetry and applies equally well to nonsupersymmetric extremal black holes. We discuss several examples which support this argument and also several caveats which could invalidate this argument.
Coherence engineering

We describe spatial coherence encoding in an LED array microscope, where a programmable array of LEDs is used in the source plane to pattern the spatial coherence of the illumination. Using this simple setup, we demonstrate various imaging functionality enabled by coherence manipulation: bright-field, darkfield, 3D phase imaging, and enhanced resolution. Our synthetic large NA imaging is an extension of the Fourier Pty-chography method to multiple coherent modes via illumination multiplexing. The result is significant decrease of both acquisition time and data capture requirements.
Chapter 2 RET-FMTC mutants Y 791 F and S 891 A activate a Src / JAK / STAT 3 pathway , independent of GDNF

The RET proto-oncogene encodes a receptor tyrosine kinase whose dysfunction plays a crucial role in the development of several neural crest disorders. Distinct activating RET mutations cause Multiple Endocrine Neoplasia type 2A (MEN2A), type 2B (MEN2B) and Familial Medullary Thyroid Carcinoma (FMTC). Despite clear correlations between the mutations found in these cancer syndromes and their phenotypes, the molecular mechanisms connecting the mutated receptor to the different disease phenotypes are far from completely understood. Luciferase reporter assays in combination with immunoprecipitation, western and immunohistochemistry analyses were performed in order to characterize the signalling properties of two FMTC associated RET mutations, Y791F and S891A, respectively, both affecting the tyrosine kinase domain of the receptor. We show that these RET-FMTC mutants are monomeric receptors which are autophosphorylated and activated independently of GDNF. Moreover, we demonstrate that the dysfunctional signalling properties of these mutants, when compared to wild type RET, involve constitutive activation of STAT3. Furthermore, we demonstrate that STAT3 activation is mediated by a signalling pathway involving SRC, JAK1 and JAK2, differing from STAT3 activation promoted by RETC634R which previously was found to be independent of SRC and JAKs. Three-dimensional modeling of the RET catalytic domain suggested that the structural changes promoted by the respective amino acids substitutions lead to a more accessible substrate and ATP binding monomeric conformation. Finally, immunohistochemical analysis of FMTC tumor samples support the in vitro data, as nuclear localized, Y705-phosphorylated STAT3 as well as a high degree of RET expression at the plasma membrane was observed.
Do we act as old as we feel? An examination of subjective age and job crafting behaviour of late career employees

ABSTRACT Ageing research calls for a focus on the mechanisms that can explain effects of ageing beyond the purely chronologic marker of age. To address this issue, the present study focuses on subjective age as a holistic construct that is related to various developmental and motivational processes and allows deeper insights into the interindividual variability of the ageing experience in older workers. Specifically, the current study examines on a sample of N = 485 late career employees (mean age 54 years), if subjective age is related to job crafting behaviours of older workers and whether job crafting is related to higher levels of work meaningfulness in late career. Results indicate that subjective age is significantly negatively related to job crafting behaviour over and above the effect of chronological age, self-rated health and workplace autonomy. Job crafting, in turn, significantly predicted work meaningfulness, above the effect of workplace autonomy. In sum, our study provides evidence for the utility of psychological representations of ageing to understand job crafting at work for an increasingly important segment of the working population.
Focusing effects of L‐dopa in Parkinson's disease

Previous fMRI motor studies in Parkinson's disease (PD) have suggested that L‐dopa may “normalize” areas of hypo‐ and hyperactivity. However, results from these studies, which were largely based on analyzing BOLD signal amplitude, have been conflicting. Examining only amplitude changes at distinct loci may thus be inadequate in fully capturing the activation changes induced by L‐dopa. In this article, we extended prior analyses on the effects of L‐dopa by investigating both amplitude and spatial changes of brain activation before and after L‐dopa. Ten subjects with PD, both on and off medication, and ten healthy, age‐matched controls performed a visuo‐motor tracking task in which they sinusoidally squeezed a bulb at 0.25, 0.5, and 0.75 Hz. This task was contrasted with static squeezing to generate fMRI activation maps. To investigate the effects of L‐dopa, we examined the amplitude and spatial variance of the BOLD response within anatomically‐defined regions of interest (ROIs). L‐dopa had significant main effects on the amplitude of BOLD signal in bilateral primary motor cortex and left SMA. In contrast, L‐dopa‐mediated spatial changes were apparent in bilateral cerebellar hemispheres, M1, SMA, and right prefrontal cortex. Moreover, L‐dopa appeared to normalize the spatial distribution of ROI activation in PD to that of the controls. Specifically, L‐dopa had a “focusing” effect on activity—an effect more pronounced than the typically‐measured fMRI amplitude changes. This observation is consistent with modeling studies, which demonstrated that dopamine increases the signal‐to‐noise ratio at the neuronal level with a resultant focusing of representations at the macroscopic level. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.
Scalable Inverse Reinforcement Learning via Instructed Feature Construction

Inverse reinforcement learning (IRL) techniques (Ng and Russell, 2000) provide a foundation for detecting abnormal agent behavior and predicting agent intent through estimating its reward function. Unfortunately, IRL algorithms suffer from the large dimensionality of the reward function space. Meanwhile, most applications that can benefit from an IRL-based approach to assessing agent intent, involve interaction with an analyst or domain expert. This paper proposes a procedure for scaling up IRL by eliciting good IRL basis functions from the domain expert. Further, we propose a new paradigm for modeling limited rationality. Unlike traditional models of limited rationality that assume an agent making stochastic choices with the value function being treated as if it is known, we propose that observed irrational behavior is actually due to uncertainty about the cost of future actions. This treatment normally leads to a POMDP formulation which is unnecessarily complicated, and we show that adding a simple noise term to the value function approximation accomplishes the same at a much smaller cost.
Resolving Executing-Committing Conflicts in Distributed Real-time Database Systems

In a distributed real-time database system (DRTDBS), a commit protocol is required to ensure transaction failure atomicity. If data conflicts occur between executing and committing transactions, the performance of the system may be greatly affected. In this paper, we propose a new protocol, called deadline-driven conflict resolution (DDCR), which integrates concurrency control and transaction commitment management for resolving executing and committing data conflicts amongst firm real-time transactions. With the DDCR, a higher degree of concurrency can be achieved, as many data conflicts of such kind can be alleviated, and executing transactions can access data items which are being held by committing transactions in conflicting modes. Also, the impact of temporary failures which occurred during the commitment of a transaction on other transactions, and the dependencies created due to sharing of data items is much reduced by reversing the dependencies between the transactions. A simulation model has been developed and extensive simulation experiments have been performed to compare the performance of the DDCR with other protocols such as the Opt [1], the Healthy-Opt [2], and the base protocol, which use priority inheritance and blocking to resolve the data conflicts. The simulation results show that the DDCR can significantly improve the system performance under different workload and workload distributions. Its performance is consistently better than the base protocol and the Opt protocols in both main-memory resident and disk-resident DRTDBS.
On the faulty sensor identification algorithm of wireless sensor networks under the PMC diagnosis model

Diagnosis is an essential subject for the reliability of a network system. Under the PMC diagnosis model, Dahbura and Masson (1984) proposed a polynomial-time algorithm with time complexity O(N2.5) to identify all the faulty nodes in an N-node network. In this paper we present a novel method to diagnose a wireless sensor network by applying the concept behind the local diagnosability, first introduced by Hsu and Tan (2007). The local diagnosability can be thought of as a local strategy toward the global system diagnosis. There is a strong relationship between the local diagnosability and the traditional diagnosability. The goal of local diagnosis is to identify the fault status of any single node correctly. Under the PMC diagnosis model, we give a sufficient condition to estimate the local diagnosability of any given sensor in a wireless sensor network. Furthermore, we use a helpful structure, called extending star, to determine the fault status of each sensor in the network. For a given sensor s whose degree is d in the network, the proposed algorithm takes time O(d) pointing out its fault status under the PMC model, provided that there is an extending star of order d rooted at s and that the time for a sensor to test another one is constant.
A Proposed Relational Fuzzy C-Means Algorithm Applied to 2D Gel Image Segmentation

One of the new and promising algorithms appeared in the area of image segmentation is the Fuzzy C-Means algorithm. This algorithm has been used in many applications such as: data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Our work in this paper will be based on the Fuzzy C-Means algorithm and by adding the relational fuzzy notion to it so as to enhance its performance especially in the area of 2-D gel images. The simulation results of comparing the Fuzzy C-Means (FCM) and the proposed algorithm Relational Fuzzy CMeans (RFCM) on 2D gel images acquired from: Human leukemias, HL-60 cell lines and Fetal alcohol syndrome (FAS) show the improvement achieved by the proposed algorithm in overcoming the over-segmentation error.
Traffic engineering for virtual private networks and random traffic demands in WDM optical core networks

Two main classes of traffic demands may be considered in data networks: virtual private networks (VPN) and random traffic requests. In this paper, we investigate traffic engineering for such traffic classes in the context of hybrid WDM optical networks. A hybrid optical network is made of a set of multi-granularity switches, each electrical cross-connect (EXC) of the network being coupled to an optical cross-connect (OXC). Our approach differs from existing approaches by combining global optimization tools with iterative optimization tools to solve the routing and wavelength assignment (RWA) problem. For this reason, a simulated annealing meta-heuristic has been developed to route lightpath demands associated to VPN traffic. On the other hand, random demands are routed sequentially by means of a Dijkstra algorithm. Hence, space-time correlation between demands as well as sub-wavelength traffic grooming have been considered in our RWA strategy.
Secure management of key distribution in cloud scenarios

Existing key distribution scheme based on key derivation has security default in cloud scenarios. The scheme distributes decryption keys to users through tags stored on cloud server. If the tag is destroyed by cloud server intentionally or unintentionally, the key distribution is destroyed too. Besides the above case, if all related tags are stored on client operated by user, the storage burden is high. To eliminate the insecurity of key distribution, we propose a novel solution based on tag derivation. In our scheme, each user needs to manage a single key and tag. Through the two information, the user can compute all authorized keys without using any information stored on cloud server. That is, our key distribution scheme is unrelated to cloud server to enhance the security of key distribution. The experiment results show that the performance of our method is better than existing methods in key distribution and query.
Developing an Evolutionary Algorithm to Search for an Optimal Multi-Mother Wavelet Packets Combination

The wavelet transform is a popular analysis tool for non-stationary data, but in many cases, the choice of the mother wavelet and basis set remains uncertain, particularly when dealing with physiological data. Furthermore, the possibility exists for combining information from numerous mother wavelets so as to exploit different features from the data. However, the combinatorics become daunting given the large number of basis sets that can be utilized. Recent work in evolutionary computation has produced a subset selection genetic algorithm specifically aimed at the discovery of small, high-performance, subsets from among a large pool of candidates. Our aim was to apply this algorithm to the task of locating subsets of packets from multiple mother wavelet decompositions to estimate cardiac output from chest wall motions while avoiding the computational cost of full signal reconstruction. We present experiments which show how a continuous assessment metric can be extracted from the wavelets coefficients, but the dual-objective nature of the algorithm (high accuracy with small feature sets) imposes a need to restrict the sensitivity of the continuous accuracy metric in order to achieve the small subset size desired. A possibly subtle tradeoff seems to be needed to meet the dual objectives.
Integrating Tsai ’ s Camera Calibration Algorithm with KLT Feature Tracking

We describe a robust and accurate algorithm to track selected features of a 3D scene in a video recording. The algorithm uses a synergistic combination of Roger Tsai’s camera calibration (TCC) algorithm and the Kanade-LucasTomasi (KLT) feature finder. Instead of trying to detect and reject outliers, we modified Tsai’s algorithm to use confidence weights which depend on the quality of the match obtained by the KLT finder. Conversely, we use the TCCcalibrated camera parameters to predict the position and shape of each feature. As a result, our algorithm can usually recover a feature after an occasional KLT failure, or even after it has been occluded for many frames.
Design Considerations for an Accident Management System Research Paper

Effective accident management requires the organization of large amounts of information. This information consists of reference information which rarely changes during an accident and decision support information which changes very rapidly. Much of the rapidly changing information is a result of collaboration between accident response personnel. In this paper we discuss accident management systems as instances of cooperative information systems and describe three designs of an accident management system called FRIEND developed at Carnegie Mellon University. We concentrate on how each design handles the rapidly changing information with different notification mechanisms to support the collaboration between end users. We identify two specific types of architectures, distributed state and stateless, and conclude with a general discussion of architectures supporting cooperative information systems.