Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamic Test of Conditional Asset Pricing Models

I use Bayesian tools to develop a dynamic testing methodology for conditional factor pricing models, in which time-varying betas, idiosyncratic risks, and factors risk premia are jointly estimated in a single step. Based on this framework, I test over fifty years of post-war monthly data some of the most common factor pricing models on size, book-to-market, and momentum deciles portfolios, both in the time series and in the cross section. The empirical results show that, a conditional specification of the recent five-factor model of Fama and French (2015) outperforms a set of theory-based competing linear pricing models along several dimensions.
Chemoprevention of Prostate Cancer: Soy Isoflavones and Curcumin

The burden of increasing morbidity and mortality due to prostate cancer imposes a need for new, effective measures of prevention in daily life. The influence of lifestyle on carcinogenesis in Asian men who migrate to Western cultures supports a causal role for dietary, environmental, and genetic factors in the epidemiology of prostate cancer. Chemoprevention, a prophylactic approach that uses nontoxic natural or synthetic compounds to reverse, inhibit, or prevent cancer by targeting specific steps in the carcinogenic pathway, is gaining traction among health care practitioners. Soy isoflavones and curcumin, staples of the Asian diet, have shown promise as functional factors for the chemoprevention of prostate cancer because of their ability to modulate multiple intracellular signaling pathways, including cellular proliferation, apoptosis, inflammation, and androgen receptor signaling. Recent evidence has revealed the DNA damage response (DDR) to be one of the earliest events in the multistep progression of human epithelial carcinomas to invasive malignancy. Soy isoflavones and curcumin activate the DDR, providing an opportunity and rationale for the clinical application of these nutraceuticals in the chemoprevention of prostate cancer.
Specification and modeling of dynamic, distributed real-time systems

Time constrained systems which operate in dynamic environments may have unknown worst case scenarios, may have large variances in the sizes of the data and event sets that they process (and thus, have large variances in execution latencies and resource requirements), and may not be statically characterizable, even by time invariant statistical distributions. The paper presents a specification language for describing environment dependent features. Also presented is an abstract model that is constructed (statically) from the specifications, and is augmented (dynamically,) with the state of environment dependent features. The model is used to define techniques for QoS (quality of service) monitoring, QoS diagnosis, and resource allocation analysis. Experimental results show the effectiveness of the approach for specification of real time QoS, detection and diagnosis of QoS failures, and restoration of acceptable QoS via reallocation of distributed computer and network resources.
Generalized detector as a spectrum sensor in cognitive radio networks

The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. Additionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR system as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detector.
Proceedings of Olkc 2007 – " Learning Fusion " the Institutionalization of Organizational Learning: a Neoinstitutional Perspective

Crossan, Lane and White (1999) presented a model of organizational learning that includes intuiting, interpreting, integrating and institutionalizing processes. Organizational learning studies that have followed Crossan, Lane and White's 1999 4I framework have explored and elaborated on the initial three processes of organizational learning. Theses studies however, have not fully elaborated on the processes of the institutionalization of organizational learning. The purpose of this study is to explore, identify and characterize the institutionalization processes that lead to the embedding of knowledge in the organizational memory. This paper discusses the theoretical framework and methodology of a doctoral research study that explores how a company institutionalized knowledge about implementing a sustainable development program.
The Efficacy of Neural Networks and Simple Technical Indicators in Predicting Stock Markets

This paper investigates the efficacy of neural networks and simple technical indicators in predicting stock market movement. The prediction system uses a back-propagation neural network and the KD and %R indicators. Our results show that monthly indicators respond too slowly to effectively capture the market trends. The %R indicator is a better market predictor than the KD indicator when they are used alone. The daily %R, weekly %R, weekly KD indicators, and their combinations can provide reasonable predictions with a percentage of correct predictions of around 60%. If the predictions of sideway-movements are excluded, the prediction accuracy can increase to about 80% Our neural network prediction system works equally well on both the TSE market and the Nasdaq market. Though specialized for the KD and %R indicators, many aspects of this methodology can be generalized to check the validity of other technical indicators.
Task Transfer via Collaborative Manipulation for Insertion Assembly

Insertion tasks are a major difficulty for automatizing manufacturing processes. The bench-mark problem in this category, peg-in-hole, is representative of the challenges that arise in uncertain assembly operations. Humans can carry out this task with ease, even in absence of visual information, relying on haptic and tactile sensing to align the peg with the hole. No generic control approach exist that can reproduce this capability in robots. In this work, we propose a system that allows to transfer this skill to a robot through collaborative task executions. The robot learns from these executions by monitoring its pose and sensed force information and encoding it as a multivariate probability distribution. This distribution is then used to realize an active correction strategy allowing the robot to execute the task autonomously. The approach is validated through a comparative study of peg-in-hole insertion using five different adaptation schemes. The results indicate a significant advantage of the learned model compared to blind random search and insertion without adaptation.
Internet Architecture and Human Rights: Beyond the Human Rights Gap

Internet architecture and infrastructure are generally not at the top of the concerns of end users, and the overlying logical arrangements of root services, domain names, and protocols remain largely invisible to its users. Recent developments, however—including massive user data leakages, hacks targeting social networking service providers, and behavioral micro‐targeting—have turned a spotlight on Internet governance defined broadly, and its relationship with civil liberties and human rights. The articles in this special issue examine the policymaking role of influential private intermediaries and private actors such as ICANN in enacting global governance via Internet architecture, exploring the implications of such a mode of governance for human rights. They consider: to what extent are human rights standards mediated and set via technical infrastructure, such as the DNS and platform policies, rather than by governmental structures? What are the implications of governance via Internet architecture for individual human rights? And what frameworks—be they legal, technological or policy‐related—are needed to address the contemporary privatization of human rights online, in order to ensure the effective protection of human rights in the digital age?
A digital robust image watermarking against desynchronization attacks

In this paper, a robust image watermarking technique using support vector regression (SVR) and particle swarm optimization is introduced to protect intellectual property rights of the gray images in discrete cosine transform domain against a variety of desynchronization attacks. After the division of the original image to 8 × 8 non-overlapping blocks, frequency coefficients of each block are found using discrete cosine transform. Positions of the inputs and output, among the low frequency coefficients which have the significant characteristics of the image, which are used to train SVR are obtained by using particle swarm optimization technique. After SVR is trained using the obtained positions of the inputs and output, watermark embedding and extracting processes are implemented using the trained SVR. Experiments implemented using the optimized coefficients selected among low frequency coefficients show that our watermarking technique has better watermark extracting success after the desynchronization attacks. Key words: Robust image watermarking, support vector regression, particle swarm optimization.
Leak diagnosis of gas transport pipelines based on Hilbert-Huang transform

In this paper, an algorithm for gas pipeline leak detection based on Hilbert-Huang transform(HHT) studied. And both Fourier transform and HHT transform can be used to decompose pipeline leakage signals. The results that Fast Fourier Transform(FFT) can not get the instantaneous frequency. But the HHT transform decomposes empirical mode decomposition (EMD) to get different scale components. And each component of Hilbert transforms are the actual meaning of the instantaneous frequency. Hilbert-Huang transform can more accurately reflect the actual frequency components of signals. And more suitable for processing non-stationary signal than FFT. And then locates leak according to the time difference of the leak acoustics arriving in the upstream and downstream end. Proved the validity of the method.
Supplementation of Antipsychotic Treatment with the Amino Acid Sarcosine Influences Proton Magnetic Resonance Spectroscopy Parameters in Left Frontal White Matter in Patients with Schizophrenia

Dysfunction of the glutamatergic system, the main stimulating system in the brain, has a major role in pathogenesis of schizophrenia. The frontal white matter (WM) is partially composed of axons from glutamatergic pyramidal neurons and glia with glutamatergic receptors. The natural amino acid sarcosine, a component of a normal diet, inhibits the glycine type 1 transporter, increasing the glycine level. Thus, it modulates glutamatergic transmission through the glutamatergic ionotropic NMDA (N-methyl-d-aspartate) receptor, which requires glycine as a co-agonist. To evaluate the concentrations of brain metabolites (NAA, N-acetylaspartate; Glx, complex of glutamate, glutamine, and γ-aminobutyric acid (GABA); mI, myo-inositol; Cr, creatine; Cho, choline) in the left frontal WM, Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy was used. Twenty-five patients randomly chosen from a group of fifty with stable schizophrenia (DSM-IV-TR) and dominant negative symptoms, who were receiving antipsychotic therapy, were administered 2 g of sarcosine daily for six months. The remaining 25 patients received placebo. Assignment was double blinded. 1H-NMR spectroscopy (1.5 T) was performed twice: before and after the intervention. NAA, Glx and mI were evaluated as Cr and Cho ratios. All patients were also assessed twice with the Positive and Negative Syndrome Scale (PANSS). Results were compared between groups and in two time points in each group. The sarcosine group demonstrated a significant decrease in WM Glx/Cr and Glx/Cho ratios compared to controls after six months of therapy. In the experimental group, the final NAA/Cr ratio significantly increased and Glx/Cr ratio significantly decreased compared to baseline values. Improvement in the PANSS scores was significant only in the sarcosine group. In patients with schizophrenia, sarcosine augmentation can reverse the negative effect of glutamatergic system overstimulation, with a simultaneous beneficial increase of NAA/Cr ratio in the WM of the left frontal lobe. Our results further support the glutamatergic hypothesis of schizophrenia.
The use of artificial intelligence’s elements to block the manifestations of individuals’ behavioral activity going beyond the quasi-stable states

The monitoring of the metastable states of individuals as a bifurcation point of development of intellectual dynamics of behavioural activity allows us to identify a set of characteristics of coherent-resonant clusters of manifestations of biophysical factors and information-cognitive nature. Conscious changes in the functioning modes of brain and human nervous system can be achieved by electromagnetic influence or purely informational influence as well as by the combined effect of both these factors. The arrangement of complex processes of informational influence is required to penetrate to the level of conscious (semantic) and unconscious (mental, emotional, meditative) interpretation of events when it is necessary to ensure sufficiently stable cognitive-behavioural stereotypes of individuals and their groups. Convergent informational and electromagnetic effects facilitate the effect on the brain, including the use of neuro-linguistic programming for this element with correction or even a complete change of the reflexive matrix (matrix of key reflexive reactions) with a corresponding change and fixation of the personality-style in the personality events to block the release of behavioural activity of individuals beyond quasi-stable states.
Health Care Utilization and Self-Assessed Health : Specification of Bivariate Models Using Copulas

The discernment of relevant factors driving health care utilization constitutes one important research topic in Health Economics. This issue is frequently addressed through specification of regression models for health care use (y – often measured by number of doctor visits) including, among other covariates, a measure of self-assessed health (sah). However, the exogeneity of sah within those models has been questioned, due to the possible presence of unobservables influencing both y and sah, and because individuals’ health assessments may depend on the quantity of medical care received. This paper addresses the possible simultaneity of (sah,y) by adopting a full information approach, through specification of the bivariate probability function (p.f.) of these discrete variables, conditional on a set of exogenous covariates (x). The approach is implemented with copula functions, which afford separate consideration of each variable margin and their dependence structure. The specification of the joint p.f. of (sah,y) enables estimation of several quantities of potential economic interest, namely features of the conditional p.f. of y given sah and x. The adopted models are estimated through maximum likelihood, with cross-section data from the Portuguese National Health Survey of 1998/99. Estimates of the margins parameters do not vary much among different copula models, while, in accordance with theoretical expectations, the dependence parameter is estimated to be negative across the various joint models. JEL classification code: I10, C16, C51.
Ant colony optimization for mapping and scheduling in heterogeneous multiprocessor systems

Heterogeneous multiprocessor systems, assembled with off-the-shelf processors and augmented with reprogrammable devices, thanks to their performance, cost effectiveness and flexibility, have become a standard platform for embedded systems. To fully exploit the computational power offered by these systems, great care should be taken when deciding on which processing element (mapping) and when (scheduling) executing the program tasks. Unfortunately, both these problems are NP-complete, and, even if they are strictly interconnected, they are normally performed separately with exact or heuristic algorithms to simplify the search for the optimum points. In this paper we present an exploration algorithm based on Ant Colony Optimization (ACO) that tries to solve the two problems simultaneously. We propose an implementation of the algorithm that gradually constructs feasible solution instances and searches around them rather than exploring a structure that already considers all the possible solutions. We introduce a two-stage decision mechanism that simplifies the data structures but lets the ant perform correlated choices for both the mapping and the scheduling. We show that this algorithm provides better and more robust solutions in less time than the Simulated Annealing and the Tabu Search algorithms, extended to support the combined scheduling and mapping problems. In particular, our ACO formulation can find, on average, solutions between 64% and 55% better than Simulated Annealing and Tabu Search.
Video Segmentation via Variational Bayes Mixture Models

Video modeling is of interest for many applications, including video indexing, video data compression, object detection, unusual-event detection and object recognition. Many approaches employ local (pixelbased) models. In Stauffer and Grimson’s work on background modeling [14], the intensity or color of each pixel is modeled as a mixture of Gaussians, where each mixing component represents one background color. This can be viewed as a temporal extension of two-dimensional (2D) image processing techniques, where each frame in the video is spatially segmented into regions and then the regions are tracked in time [15][4]. Attention has been addressed recently on the simultaneous analysis of the spatial and temporal dimensions, by considering a video sequence as a spatio-temporal block of pixels [8][13]. Greenspanet al. [6] proposed probabilistic models for such video blocks, using piecewise Gaussian mixture models (GMMs) for which each mixture component represents one homogeneous region within the block. However, model selection, i.e., choosing the proper number of mixture components, still remains a difficulty in this previous work. The maximum-likelihood (ML) based expectation-maximization (EM) algorithm [3], employed in this previous work to obtain the GMM parameters, assumes knowledge of the number of mixing components, although in practice the “true” number of components is unknown and varies with an object’s appearance/disappearance in the video. In this paper we employ spatio-temporal GMMs for each video stack and use variational Bayes (VB) inference to approximate the full posterior distribution of the GMM parameters [2]. In the VB analysis the proper number of mixture components is determined by computation of the model evidence [10].
Dynamic Task Scheduling and Processing Element Allocation for Multi-Function SoCs

This work is motivated by the rapid increasing of the design complexity of many embedded systems. It aims at the proposing of solutions to resolve the hardware contention issues of non-preemptive processing elements shared among tasks and the cost optimization. A software solution based on the starting time management is proposed to interleave task executions on processing elements. Algorithms are proposed to determine the required processing elements of selected types, when there is no knowledge on the releasing time of any task: When task release orders are known a priori, an optimal algorithm is presented if processing elements have the same cost; otherwise, a pseudo-polynomial-time algorithm based on dynamic programming is presented for optimal solutions. The performance of the algorithms is also evaluated for general cases
Virtual currency and reputation-based cooperation incentives in user-centric networks

Cooperation incentives are essential in user-centric networks to motivate users to share services and resources (including bandwidth, computational power, and storage space) and to avoid selfish nodes to hinder the functioning of the entire system. Virtual currency and reputation mechanisms are commonly adopted in online communities to boost participation, but their joint application has not been deeply explored, especially in the context of wireless communities, where not only the services, but even the enabling infrastructure is opportunistically built by community members. This paper investigates the combined use of virtual currency and reputation-based incentives in the specific context of a community of users with Wi-Fi enabled devices capable of establishing ad-hoc connections.
MEDARM: a rehabilitation robot with 5DOF at the shoulder complex

A key approach for reducing motor impairment and regaining independence after stroke is frequent and repetitive functional training. A number of robotic devices have been developed to assist therapists with the labourious task of providing treatment. Although robotic technology is showing significant potential, its effectiveness for upper limb rehabilitation is limited in part by the inability to make functional reaching movements. A major contributor to this problem is that current robots do not replicate motion of the shoulder girdle despite the fact that the shoulder girdle plays a critical role in stabilizing and orienting the upper limb during activities of daily living. To address this issue, a new adjustable robotic exoskeleton called MEDARM is proposed for motor rehabilitation of the shoulder complex. MEDARM provides independent control of six degrees of freedom (DOF) of the upper limb: two at the sternoclavicular joint, three at the glenohumeral joint and one at the elbow. Its joint axes are optimally arranged to mimic the natural upper- limb workspace while avoiding singular configurations and while maximizing manipulability. This mechanism also permits reduction to planar shoulder/elbow motion in any plane by locking all but the last two joints. Electric motors actuate the joint using a combination of cable and belt transmissions designed to maximize the power-to-weight ratio of the robot while maintaining backdriveability and minimizing inertia. Thus, the robot can provide any level of movement assistance and gravity compensation. This paper describes the proposed technical design for MEDARM.
In vivo evidence for cooperation of Mia40 and Erv1 in the oxidation of mitochondrial proteins

The mechanisms that underlie the oxidative biogenesis of mitochondrial proteins catalyzed by disulfide carrier Mia40 and thiol oxidase Erv1 are not fully understood. This study identifies dynamics of the Mia40–substrate intermediate complex and shows that Erv1 directly participates in Mia40–substrate dynamics by forming a ternary complex.
Visualizing attractive periods of popular photo spots using Flickr data

The popularity of cameras with GPS receivers and the emergence of photo-sharing websites such as Flickr have created enormous collections of photos that are annotated with GPS locations, time-stamps, photographers, etc. Thus, one can obtain a large number of observations of where and when people take photos. Since people tend to take photos when they meet visually interesting things on their sightseeing tours, attention has recently been devoted to constructing sightseeing guide systems that exploit the information revealed by the collective behavior of users in photo-sharing websites (see, e.g., [Lu et al. 2010]). Previous work [Crandall et al. 2009] discovered popular photo spots from a large number of geo-tagged photos, and visualized them with the found representative images on maps.