Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty, Expectations, and Financial Instability: Reviving Allais's Lost Theory of Psychological Time

List of TablesList of FiguresPrefaceAcknowledgmentsIntroductionGlossary of Mathematical Symbols in Order of AppearancePart 1. The Progressive Emergence of Expectations in Economic Theory1. Expectations Before the Rational Expectations Revolution2. Rational Expectations Are Endogenous to and Abide by "the" ModelPart 2. Allais's Theory of "Expectations" Under Uncertainty3. Macrofoundations of Monetary Dynamics4. Microfoundations of Monetary Dynamics: The HRL Formulation of the Demand for Money5. The Fundamental Equation of Monetary Dynamics6. Joint Testing of the HRL Formulation of the Demand for Money and of the Fundamental Equation of Monetary DynamicsPart 3. Transposing the HRL Formulation to Financial Markets: Preliminary Steps7. Allais's HRL Formulation: Illustration of Its Dynamic Properties by an Example of Hyperinflation (Zimbabwe 2000-2008)8. The HRL Formulation and Nominal Interest RatesPart 4. The HRL Formulation and Financial Instability9. Perceived Returns and the Modeling of Financial Behavior10. Downside Potential Under Risk: The Allais Paradox and Its Conflicting Interpretations11. Downside Potential Under Uncertainty: The Perceived Risk of Loss12. ConclusionAppendix A: How to Compute Zn and znAppendix B: Nominal Interest Rates and the Perceived Rate of Nominal GrowthAppendix C: ProofsAppendix D: Comparison Between the Kalman Filter and Allais's HRL AlgorithmAppendix E: A Note on the Theory of Intertemporal ChoiceAppendix F: Allais's Cardinal Utility FunctionNotesBibliographyIndex
Recognition of EOG based reading task using AR features

Eye movements play an important role in evaluating the process of reading. By visual inspection of the eye movements, it is possible to differentiate the reading process of different persons. The eye movements can be considered as objective tools for understanding the reading process. However, most of the eye movements are involuntary and out of our conscious control. Hence the reading process is better understood when the analysis of eye movements is automated. This research work presents a pilot study conducted in process of automating eye movement analysis to get an insight into the reading process. Electrooculogram (EOG) has been used for recording the eye movements from a group of 40 volunteers. Several autoregressive (AR) features based on Yule walker's method, Burg's method, modified covariance method and Linear Predictor Coefficients obtained using Levinson-Durbin recursion methods have been extracted from the raw EOG. The horizontal and vertical modes were then recognized by employing a recurrent Elman neural network. Simulation results show a classification accuracy of 99.95% which indicates the suitability of proposed scheme for human-computer interface applications.
A Scalable Method for Partitioning Workflows with Security Requirements over Federated Clouds

The significant increase in the use of cloud computing, has led to an interest in partitioning applications over a set of public and private clouds in order to meet a range of non-functional requirements including performance (for example where private cloud resources alone are insufficient), dependability (e.g. To allow the application to continue to operate even if one cloud fails) and security (for example to ensure that sensitive data is restricted to sufficiently secure clouds and networks). This paper describes a novel deployment planning algorithm to partition complex workflow-based applications over federated clouds, while meeting security requirements. The security issues are based on our previous work which extends the Bell-La Padula model to encompass cloud computing. Selecting the cheapest option for partitioning a workflow over a set of resources has been shown to be an NP-hard problem, which can take impractically long for partitioning large workflows over multiple clouds. We therefore introduce a novel adaptive partitioning algorithm to handle these large workflow applications, which significantly reduces the time required to choose a sufficiently good partitioning option. This is based on generating an initial partitioning, and then adapting it to see if a better solution can be found by bringing together on the same node services with significant communication costs. The algorithm has been implemented and evaluated by using both randomly generated and real world scientific workflows. The experiment results show that our algorithm is thousand times quicker than the exhaustive algorithm presented in our previous work. Yet, on average it generates only 25% more costly solutions. We also compared this algorithm with two other methods commonly used to partition workflows over a set of clouds.
Extending Access Management to maintain audit logs in cloud computing

Considering the most often talked about security risks in cloud computing, like, security and compliance, viability, lack of transparency, reliability and performance issues. Bringing strong auditability in cloud services can reduce these risks to a great extent. Also, auditing, both internally and externally is generally required and sometimes unavoidable looking into the present day competition in the business arena. Auditing in web based and cloud based usage environments focuses mainly on cost of a service which determines the overall expenditure of the user organization. However, the expenditure can be controlled by a collaborative approach between the provider company and the user organization by constantly monitoring the end user access and usage of subscribed cloud services. Though, many cloud providers will claim of having a robust auditable feature, the generic verifiability with sustainable long term recording of usage logs do not exist at all. Certain access management models can be perfectly extended to maintain audit logs for long terms. However, maintaining long term logs certainly has storage implications, especially with larger organizations. The storage implications need to be studied.
A Multi-Objective Genetic Local Search Algorithm for Optimal Feature Subset Selection

Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called ultiobjective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.
Learning Relative Similarity from Data Streams: Active Online Learning Approaches

Relative similarity learning, as an important learning scheme for information retrieval, aims to learn a bi-linear similarity function from a collection of labeled instance-pairs, and the learned function would assign a high similarity value for a similar instance-pair and a low value for a dissimilar pair. Existing algorithms usually assume the labels of all the pairs in data streams are always made available for learning. However, this is not always realistic in practice since the number of possible pairs is quadratic to the number of instances in the database, and manually labeling the pairs could be very costly and time consuming. To overcome the limitation, we propose a novel framework of active online similarity learning. Specifically, we propose two new algorithms: (i)~PAAS: Passive-Aggressive Active Similarity learning; (ii)~CWAS: Confidence-Weighted Active Similarity learning, and we will prove their mistake bounds in theory. We have conducted extensive experiments on a variety of real-world data sets, and we find encouraging results that validate the empirical effectiveness of the proposed algorithms.
Properties of Bars and Bulges in the Hubble Sequence

Properties of bars and bulges in the Hubble sequence are discussed, based on the analysis of 216 disk galaxies (S0s and spirals from NIRS0S and OSUBGS surveys, respectively). For that purpose we have collected together, and completed when necessary, the various analysis we have previously made separately for early and late types. We find strong photometric and kinematic evidence of pseudobulges in the S0-S0/a galaxies: their bulges are on average fairly exponential, inner disks are common (in 56%), and in many of the galaxies the bulges are rotationally supported. This would be difficult to understand in such gas poor galaxies as in S0s, i f these pseudobulge candidates were formed by star formation in the disk in a a similar manner as in spirals. A more likely explanation is that pseudobulges in the early-type galaxies are bar-related structures, connected to the evolution of bars, which interpretation is supported by our Fourier analysis and structural decompositions. Bars in the early-type galaxies are found to have many characteristics of evolved systems: (1) they have flat-top/double peaked Fourier amplitude profiles, (2) bars have typically sharp outer cut-offs, (3) the higher Fourier modes appear in the amplitude profiles, and (4) many bars have also ansae-type morphologies. We show the distributions of bar strength in different Hubble type bins using four bar strength indicators, Qg, A2, fbar and the bar length, which are expected to give important clues for understanding the mechanism of how bars evolve.
Advanced Data Integration and Data Mining for Enviromental Scenarios

The use of data mining techniques in environmental applications of IT, and specifically in hydro-meteorological predictions, is not a new topic. In this paper however we present the novel use of data mining in the ADMIRE Project and its scenarios. In Orava scenario we try to predict the change of water level and water temperature in the OravaRiver below the Orava reservoir, and how they react to discharge from the reservoir. In second scenario - Radar, we are predicting rainfall in short term. The last scenario - SVP, is modeling situation of melting snow and caused inflow into reservoir.
Visually Fingerprinting Humans without Face Recognition

This paper develops techniques using which humans can be visually recognized. While face recognition would be one approach to this problem, we believe that it may not be always possible to see a person? s face. Our technique is complementary to face recognition, and exploits the intuition that human motion patterns and clothing colors can together encode several bits of information. Treating this information as a "temporary fingerprint", it may be feasible to recognize an individual with reasonable consistency, while allowing her to turn off the fingerprint at will. One application of visual fingerprints relates to augmented reality, in which an individual looks at other people through her camera-enabled glass (e.g., Google Glass) and views information about them. Another application is in privacy-preserving pictures ? Alice should be able to broadcast her "temporary fingerprint" to all cameras in the vicinity along with a privacy preference, saying "remove me". If a stranger? s video happens to include Alice, the device can recognize her fingerprint in the video and erase her completely. This paper develops the core visual fingerprinting engine ? InSight ? on the platform of Android smartphones and a backend server running MATLAB and OpenCV. Results from real world experiments show that 12 individuals can be discriminated with 90% accuracy using 6 seconds of video/motion observations. Video based emulation confirms scalability up to 40 users.
Policy Analysis for Social Workers

Introduction Section I. The World of Policy Analysis Chapter 1: Science, Values, and Policy Analysis Chapter 2: The Purpose of Policy Analysis Chapter 3: Approaches to Policy Analysis Section II. Policy as Product Chapter 4: Evaluating Policy Proposals Chapter 5: Matching Policy Proposals to Problems Chapter 6: Costs, Benefits, and Risks Chapter 7: Applying Cost-Benefit Analysis Section III. Policy as Process Chapter 8: Making Policy Chapter 9: Implementing Policy Section IV. Policy as Performance Chapter 10: Approaches to Evaluation Chapter 11: Evaluation, Values, and Theory Epilogue: A Holistic Framework for Policy Analysis Appendix A: CSWE Core Competencies Appendix B: Historical Overview of Policy Analysis and Policy Studies Web Sites & Glossary References Index
Global illumination rendering via indirect light field regression

Global illumination is important and hard to render in computer graphics. We propose a novel technique to generate high quality global illumination images using regression analysis. Our algorithm has two stages. During sampling stage, we get the indirect light field and use it to train an artificial neural network model. In reconstruction stage, the neural network model is used to synthesize the final image. The experiments show our algorithm generates higher quality images than the previous methods.
Diacritics Restoration in Romanian Texts

There are several languages that use diacritical characters outside the ASCII charset. For some of the languages, most diacritical characters can be deterministically recovered but in general, this is not the prevailing case. However, the difficulty of the task differs from language to language depending on the functional role of the diacritical characters. For Romanian, automatic restoration of the diacritics is a real challenge, both because of their frequency and due to their significant contribution to the morpho-lexical and semantic disambiguation of the words. In Romanian, every third word might contain at least one diacritical character and for large texts that lack diacritics, to insert them manually is highly time-consuming, boring and error-prone. We present a professional implementation, embedded into MS Office environment, which builds on our tiered tagging technologies.
Exploring Deterministic Shared Memory Programming Model

Deterministic parallelism promises many benifits for parallel programming. Exist deterministic runtimes, however, either require modifying programs to use a restricted set of synchronization primitives, or limit scalability by relying on a centralized, deterministic thread scheduler. We addressed these challenges with single producer multi-consumer (SPMC) virtual memory as a possible foundation for scalable deterministic parallelism. The SPMC foundation avoids the introduction of read/write and write/write data races, and permits peer-topeer “dialog” short-circuiting the process/thread hierarchy. In this paper, we explore high level runtime-DetSM-atop the SPMC foundation to support deterministic shared memory parallel programming. We introduce a generalized shared memory unit or SMU abstraction to represent memory deterministically shared among threads, and a specific DetArray abstraction to allow concurrent threads updating disjoint continuous elements in an array, with certain programmability and efficiency tradeoffs. Preliminary results suggest that DetSM atop the SPMC maybe realistic and useful, achieving better run-time than Dthreads for dedup, and better performance than the original Determinator for quick sort and merge sort.
Proper motion studies of outflows from Classical T Tauri stars

Received date ;accepted date Abstract. In a previous paper (McGroarty & Ray, 2004) we examined the environment of a number of evolved low-mass young stars, i.e. Classical T Tauri Stars, to see if they are capable of driving parsec-scale outflows. These stars - CW Tau, DG Tau, DO Tau, HV Tau C and RW Aur - were previously known to drive only "micro- jets" or small-scale outflows of � 1 ' or 0.04 pc at the distance of the Taurus-Auriga Cloud. We found that they drive outflows of 0.5pc - 1pc, based on the morphology and alignment of newly discovered and previously known HH objects with these sources and their "micro-jets". Here, we use a cross-correlation method to determine the proper motions of the HH objects in these five outflows (HH220, HH229, HH702, HH705 and HH826 - HH835) which in turn allows us to confirm their driving sources. Moreover, the tangential velocities of HH objects at large distances from their origin are currently poorly known so these proper motions will allow us to determine how velocities evolve with distance from their source. We find tangential velocities of typically 200 kms −1 for the more distant objects in these outflows. Surprisingly, we find similar tangential velocities for the "micro-jets" that are currently being ejected from these sources. This leads us to suggest that either the outflow velocity was much higher 10 3 years ago when the more distant objects were ejected and that these objects have decelerated to their current velocity or that the outflow velocity at the source has remained approximately constant and the more distant objects have not undergone significant deceleration due to interactions with the ambient medium. Numerical simulations are needed before we can decide between these scenarios.
Advance-Purchase Discounts and Monopoly Allocation of Capacity

Optimal pricing by a monopoly airline that faces capacity constraints during the peak demand period is studied. The existence of capacity constraints means that in order to expand outp ut the airline must divert demand from the peak period to the off-peak period. A particular advance-purchase discount policy is shown to be the profit-maximizing method of selling tickets. If the advance-purchase requirement were infeasible, output and total surpl us would both be lower. Copyright 1993 by American Economic Association.
Prediction of Disease-Related Interactions between MicroRNAs and Environmental Factors Based on a Semi-Supervised Classifier

Accumulated evidence has shown that microRNAs (miRNAs) can functionally interact with a number of environmental factors (EFs) and their interactions critically affect phenotypes and diseases. Therefore, in-silico inference of disease-related miRNA-EF interactions is becoming crucial not only for the understanding of the mechanisms by which miRNAs and EFs contribute to disease, but also for disease diagnosis, treatment, and prognosis. In this paper, we analyzed the human miRNA-EF interaction data and revealed that miRNAs (EFs) with similar functions tend to interact with similar EFs (miRNAs) in the context of a given disease, which suggests a potential way to expand the current relation space of miRNAs, EFs, and diseases. Based on this observation, we further proposed a semi-supervised classifier based method (miREFScan) to predict novel disease-related interactions between miRNAs and EFs. As a result, the leave-one-out cross validation has shown that miREFScan obtained an AUC of 0.9564, indicating that miREFScan has a reliable performance. Moreover, we applied miREFScan to predict acute promyelocytic leukemia-related miRNA-EF interactions. The result shows that forty-nine of the top 1% predictions have been confirmed by experimental literature. In addition, using miREFScan we predicted and publicly released novel miRNA-EF interactions for 97 human diseases. Finally, we believe that miREFScan would be a useful bioinformatic resource for the research about the relationships among miRNAs, EFs, and human diseases.
Functional Redundancy of Septin Homologs in Dendritic Branching

Septins are cytoskeletal GTPases present in nonpolar heteromeric complexes that assemble in a palindromic fashion from two to eight subunits. Mammalian septins function in several fundamental cellular processes at the membrane-cytoskeleton interface including dendritic branching in neurons. Sequence homology divides the 13 mammalian septin genes into four homology groups. Experimental findings suggest that septin function is redundant among septins from one homology group. This is best understood for the isoforms of the SEPT2 group, which form a homodimer at the center of septin complexes. In vitro, all SEPT2-group septins form recombinant hexameric complexes with two copies of SEPT6 and SEPT7. However, it remains unclear to what extent homologs septins can substitute for each other in specific cellular processes. Here, we use the experimental paradigm of dendritic branching in hippocampal rat neurons to ask, to what extent septins of the SEPT2-group are functionally redundant. Dendritic branching is significantly reduced when SEPT5 is downregulated. In neurons expressing SEPT5-shRNA, simultaneously expressed SEPT2-GFP, and SEPT4-GFP colocalize with SEPT7 at dendritic spine necks and rescue dendritic branching. In contrast, SEPT1-GFP is diffusely distributed in the cytoplasm in SEPT5 downregulated neurons and cannot rescue dendritic branching. Our findings provide a basis for the study of septin-specific functions in cells.
Benchmarking the Accuracy of PCA Generated Statistical Compact Model Parameters Against Physical Device Simulation and Directly Extracted Statistical Parameters

Statistical variability is a major challenge for CMOS scaling and integration. In order to achieve variability aware design, it's critical important to reliably transfer device characteristics statistical variability information into compact models. A PCA based statistical compact modeling strategy is benchmarked against 'atomistic' device simulation and direct statistical parameter extraction strategy. The results indicate that PCA based approach may introduce considerable error in tail of distribution, which in turn may generate pessimistic or optimistic conclusions in statistical circuit simulation.
Narrow THz Spectral Signatures Through an RNA Solution in Nanofluidic Channels

Terahertz (THz) transmission measurements are carried out on double-stranded, small-interfering (si)-RNA (20-25 bp) samples suspended in buffer solution and confined to the channels of a nanofluidic chip having channel dimensions ~600 × 500 nm and a pitch of ~1.2 ¿m. The transmission is measured between 800 and 1090 GHz, using a coherent photomixing spectrometer tuned in 0.5 GHz steps. Five narrow spectral signatures are observed that repeated in two successive experiments. Remarkably, the linewidth of some of these features is approximately 10 GHz, making them comparable to if not narrower than the sharpest signatures ever reported at THz frequencies in the solid or liquid states at room temperature.
Accelerating optimistic HLA-based simulations in virtual execution environments

High Level Architecture (HLA)-based simulations employing optimistic synchronization allows federates to process event and to advance simulation time freely at the risk of over-optimistic execution and execution rollbacks. In this paper, an adaptive resource provisioning system is proposed to accelerate optimistic HLA-based simulations in Virtual Execution Environment (VEE). A performance monitor is introduced using a middleware approach to measure the performance of individual federates transparently to the simulation application. Based on the performance measurements, a resource manager distributes the available computational resources to the federates, making them advance simulation time with comparable speeds. Our proposed approach is evaluated using a real-world simulation model with various workload inputs and different parameter settings. The experimental results show that, compared with distributing resources evenly among federates, our proposed approach can accelerate the simulation execution significantly using the same amount of computational resources.