Miracle-dz/UniFAR-FTUs huggingface dataset:
DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.
Delayed exponential fitting by best tensor rank-(R1, R2, R3) approximation

We present a subspace-based scheme for the estimation of the poles (angular-frequencies and damping-factors) of a sum of damped and delayed sinusoids. In our model each component is supported over a different time frame, depending on the delay parameter. Classical subspace based methods are not suited to handle signals with varying time-support. In this contribution, we propose a solution based on the best rank-(R/sub 1/, R/sub 2/, R/sub 3/) approximation of a partially structured Hankel tensor on which the data are mapped. We show, by means of an example, that our approach outperforms the current tensor and matrix-based approaches in terms of the accuracy of the damping parameter estimates.
Learning of Leg Stiffness and Damping to Match Robot Dynamics

The spring-loaded inverted pendulum model, a simplified robot dynamics model, is regularly employed for the purpose of controlling the motion of legged robots. It has two key parameters to be determined, namely, spring stiffness and spring damping. For different robot systems, model parameters need to be set to match the robot’s dynamic characteristics. Nevertheless, it’s hard to match the model parameters with the robot’s dynamic characteristics through manual adjustment in engineering practice. Inspired by animals’ hierarchical and modular motion control features, this paper designs a hierarchical motion control framework for robot dynamics matching. The spring stiffness and damping of the SLIP model are learned through the deep reinforcement learning algorithm to match the control model with the robot’s dynamic characteristics. Finally, the performance of the hierarchical motion control framework is tested on a one-legged robot by using dynamics simulation software through comparative experiments. The simulation experimental results show that the robot with learned model parameters has a less mechanical cost of transport and better motion stability than the robot with fixed model parameters.
Dissipative Control of Stochastic Markov Jump Systems with Input Saturation and Quantized Measurements

This paper addresses the strictly dissipative control problem for continuous-time stochastic Markov jump systems with input saturation and quantized measurements. A modified sector condition is used to deal with the input saturation. By using a mode-dependent Lyapunov function, new results on the synthesis are proposed with satisfied dissipative performance under a unified convex optimization framework. Finally, simulation studies are conducted to illustrate the effectiveness of the proposed approaches.
Sliding-Mode Control of PMSM Speed Loop Based on Adaptive Power Reaching Law

In order to solve the problems of poor dynamic control and large chattering in the speed control system of permanent magnet synchronous motor (PMSM), a sliding-mode control strategy based on adaptive power reaching law is developed in this paper. First, the motion of the system state at different stages under the action of the traditional power-reaching aw is analyzed. Second, a new reaching law is designed using the mapping relationship of functions, and the reaching law parameters can be dynamically adjusted according to the system state. Then, the reaching law is used to design the velocity loop sliding-mode controller and perform the Lyapunov stability analysis. Finally, the motor speed is controlled by an adaptive power-law speed-loop sliding-mode controller with Simulink software and compared with a conventional power-law reaching-law controller. The results show that the new controller designed in this paper reduces the speed overshoot after starting the motor, enhances the dynamic response of the system and effectively weakens the sliding-mode chattering, and has good dynamic and static performance.
Custom-made Near Infrared Spectroscope as a Tool for Obtaining Information Regarding the Brain Condition

We can obtain valuable information about the human brain using functional Near Infrared Spectroscopy (fNIRS). This paper describes the theoretical basis associated with this neuroimaging method through a custom-made prototype of a single-channel fNIRS device. The optodes were soldered to a milled Printed Circuit Board (PCB) and enclosed in a 3D printed housing. Using this fNIRS device, we performed a preliminary study to measure emotional responses from participants. Our results suggest that fNIRS allows for accurate measurement of emotions evoked by positive and negative images.
The Interaction of Implicit Learning , Explicit Hypothesis Testing Learning , and Implicit-to-Explicit Knowledge Extraction

To further explore the interaction between the implicit and explicit learning processes in skill acquisition (which have been tackled before, e.g., in Sun et al 2001, 2005), this paper explores details of the interaction of different learning modes: implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction. Contrary to the common tendency in the literature to study each type of learning in isolation, this paper highlights the interaction among them and various effects of the interaction on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit learning processes; moreover, it also uniquely embodies a bottom-up (implicit-to-explicit) learning approach in addition to other types of learning. The paper shows that this model accounts for various effects in the human behavioral data from the psychological experiments with the process control task, in addition to accounting for other data in other psychological experiments (which has been reported elsewhere). The paper shows that to account for these effects, implicit learning, bottom-up implicit-to-explicit extraction, and explicit hypothesis testing learning are all needed.
Good and Bad Uncertainty : Macroeconomic and Financial Market Implications I

Does macroeconomic uncertainty increase or decrease aggregate growth and asset prices? To address this question, we decompose aggregate uncertainty into ‘good’ and ‘bad’ volatility components, associated with positive and negative innovations to macroeconomic growth. We document that in line with our theoretical framework, these two uncertainties have opposite impact on aggregate growth and asset prices. Good uncertainty predicts an increase in future economic activity, such as consumption, output, and investment, and is positively related to valuation ratios, while bad uncertainty forecasts a decline in economic growth and depresses asset prices. Further, the market price of risk and equity beta of good uncertainty are positive, while negative for bad uncertainty. Hence, both uncertainty risks contribute positively to risk premia, and help explain the cross-section of expected returns beyond cash flow risk.
A Dual-Polarization-Switched Beam Patch Antenna Array for Millimeter-Wave Applications

Increasing data transfer rates for future millimeter-wave communications requires high capacity radio channels and user-centric communication network environments. Therefore, special requirements, such as wide-angle beam steering, wide operational frequency bandwidth, and simultaneous operation with orthogonal polarizations, are essential for telecommunication antennas. In this communication, we present a compact patch antenna array with a Butler matrix (BM) feed network for the frequency band of 26–31.4 GHz. The 16-element planar array has been designed to operate with the two linear orthogonal polarizations and provide ±42° beam switching. To ensure the wideband operation, a novel combination of planar couplers, crossovers, and phase shifters is designed to form the BM. A new phase-shifter topology is used which is based on a combination of open-short stubs and the Shiffman phase shifter. The design is fabricated on a low-cost multilayer board with a size of <inline-formula> <tex-math notation="LaTeX">$120 \times 70 \times 1.62$ </tex-math></inline-formula> mm<sup>3</sup>. The size of the feeding network, which is implemented on a single board, is <inline-formula> <tex-math notation="LaTeX">$76 \times 23 \times 0.1$ </tex-math></inline-formula> mm<sup>3</sup>. The experimental measurements of return loss, mutual coupling, and radiation patterns confirm simulated results.
Identification of Inhibiting Factors of a Null Significant Tornado Event

A significant tornado outbreak was forecast over a large portion of the Central and Northern Plains on 5 June 1999. The outbreak was forecast to develop ahead of a strong upper-level cyclone moving through the central Rocky Mountains, in an atmosphere characterized by strong deep-layer shear and extreme instability. Although severe weather did occur, and some of the activity was significant, in most of the high risk area only isolated tornadoes were observed. This study will show that despite favorable deep shear and instability, mesoscale and storm-scale forcing was unable to overcome a pronounced lack of favorable synoptic scale dynamic support. This maintained a persistent capping inversion, and likely prevented a significant tornado outbreak. The lack of favorable dynamic support is related to the passage of a migratory short-wave ridge over the risk area during peak heating. The absence of large-scale ascent associated with the short-wave ridge is identified using several tools, including 3-hourly RUC model analyses, 6-hourly radiosondes, and visible and water vapor satellite images. The 6 and 12 hour NCEP numerical model guidance available to operational forecasters at 1200 UTC 5 June will also be compared to the observational data to show the evolution of the thermodynamic differences that developed during the period. This
COMPARATIVE ANALYSIS OF EMPLOYEES’ PERSPECTIVE ON ASSET-LIABILITY MANAGEMENT IN BANKS

Asset-Liability Management is critical for the successful working of banks. The Indian Financial system is very dynamic and growing rapidly. Banks focus on both the assets and liabilities due to liquidity risk, interest rate risk and foreign exchange risk. The implementation and understanding of ALM policies, and procedures will provide insight into the approach of banks towards ALM. A primary survey is conducted among employees of twelve public and twelve private banks to collect data related to employees' experience, understanding, and knowledge about the ALM. The questionnaire used in the study carries Yes/No type questions, multiple-choice questions, checklist questions, scaled questions. The data is analyzed using SPSS. The results suggest that public and private banks' employees have a similar understanding and knowledge of the ALM. Both types of banks follow identical practices and policies in the implementation of ALM with few deviations. The employees of the banks were hesitant to answer complex questions, and therefore questionnaire has to be kept generic and straightforward. This paper studies ALM in banks using primary data. All other studies have been conducted using secondary data or literature surveys. These studies did not feature the banks' approach in ALM policies, practices, and procedures.
A meteorological estimation of relevant parameters for snow models

Relevant meteorological parameters have been analyzed to provide boundary conditions in real time for an energy, mass and stratigraphical model of snow cover at locations surrounded by meteorological observation points. From the available observation data, this analysis provides hourly meteorological information on every Alpine massif for six different aspects at 300 m elevation intervals. A numerical snow model has been run with these estimated meteorological data for numerous locations in the French Alps during the last ten years. Comparisons with observed snow characteristics (e.g., depth and stratigraphy) have proved the potential of the method.
Seizure-onset mapping based on time-varying multivariate functional connectivity analysis of high-dimensional intracranial EEG: a Kalman filter approach.

The visual interpretation of intracranial EEG (iEEG) is used clinically to map the regions of seizure onset targeted for resection during epilepsy surgery. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional-connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signal based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared in seizure simulations for their ability to reconstitute the designed seizure signal connections from noisy data. Next, the onset of a 113-channel iEEG seizure recorded in a patient rendered seizure-free after surgery was estimated with the out-degree, a graph-theory index of outward directed connectivity. Simulation results indicated high mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.
Temporal and Spatial Expression Patterns of Transgenes Containing Increasing Amounts of the Drosophila Clock Geneperiod and a lacZ Reporter: Mapping Elements of the PER Protein Involved in Circadian Cycling

Rhythmic oscillations of the PER protein, the product of theDrosophila period (per) gene, in brain neurons of the adult fly are strongly involved in the control of circadian rhythms. We analyzed temporal and spatial expression patterns of three per–reporter fusion genes, which share the same 4 kb regulatory upstream region but contain increasing amounts ofper’s coding region fused in frame to the bacteriallacZ gene. The fusion proteins contained either the N-terminal half (SG), the N-terminal two-thirds (BG), or nearly all (XLG) of the PER protein. All constructs led to reporter signals only in the known per-expressing cell types within the anterior CNS and PNS. Whereas the staining intensity of SG flies was constantly high at different Zeitgeber times, the in situ signals in BG and XLG flies cycled with ∼24 hr periodicity in the PER-expressing brain cells in wild-type andper01 loss of function flies. Despite the rhythmic fusion-gene expression within the relevant neurons ofper01 BG flies, their locomotor activity in light/dark cycling conditions and in constant darkness was identical to that of per01 controls, uncoupling protein cycling from rhythmic behavior. The XLG construct restored weak behavioral rhythmicity to (otherwise)per01 flies, indicating that the C-terminal third of PER (missing in BG) is necessary to fulfill the biological function of this clock protein.
Ultraviolet cascade in the thermalization of the classical varphi4 theory in 3+1 dimensions

We investigate the dynamics of thermalization and the approach to equilibrium in the classical (cid:1) 4 theory in 3 (cid:1) 1 spacetime dimensions. The nonequilibrium dynamics is studied by numerically solving the equations of motion in a light-cone-like discretization of the model for a broad range of initial conditions and energy densities. A smooth cascade of energy towards the ultraviolet is found to be the basic mechanism of thermalization. After an initial transient stage, at a time scale of several hundred inverse masses, the squared magnitude of the ﬁeld spatial gradient becomes larger than the nonlinear term and there emerges a stage of universal cascade, independent of the details of the initial conditions. As the cascade progresses, the modes with higher wave numbers, but well behind the forefront of the cascade, exhibit weaker and weaker nonlinearities well described by the Hartree approximation, while the infrared modes retain strong self-interactions. As a consequence, two time scales for equilibration appear as characteristic of two time-dependent wave number regions. For k 2 * (cid:1) 2 (cid:2) t (cid:3) , we observe an effective equilibration to a time-dependent powerlike spectrum with a time scale in the hundreds of inverse masses; cutoff effects are absent and the Hartree approximation holds for k 2 (cid:4) (cid:1) 2 (cid:2) t (cid:3) . On the other hand, infrared modes with k 2 & (cid:1) 2 (cid:2) t (cid:3) equilibrate only by time scales in the millions of inverse masses when the cutoff effects become dominant and complete thermalization is setting in. Accordingly, we observe in the ﬁeld correlator a relatively large and long-lived deviation from the Hartree behavior of a nonperturbative character. There corresponds an effective mass governing the long distance behavior of the correlator which turns out to be signiﬁcantly smaller than the Hartree mass which is exhibited by the modes with k 2 * (cid:1) 2 (cid:2) t (cid:3) . Virialization and the equation of state start to set in much earlier than thermalization. The applicability of these results in quantum ﬁeld theory for large occupation numbers and small coupling is analyzed.
The Rise and Fall of a Policy Rule : Monetarism at the St . Louis Fed , 1968-1986

“Once the quantity theory regained academic respectability, it was obliged to resume responsibility for the short-run forecasting of aggregate movements of prices and quantities This it has begun to do, most importantly through the research work of the Federal Reserve Bank of St. Louis, and with appreciable success; but it has been lured into playing in a new ballpark, and playing according to a different set of rules than it initially established for itself [I]ts own success is likely to be transitory, precisely because it has relied on the same mechanisms of intellectual conquest as the [Keynesian] revolution itself and has also espoused a methodology that has put it in conflict with long-run trends in the development of the subject. ”—Harry Johnson (1971, pp. 12-13)
The Determinants of Child Labor in the Northeast and Southeast Regions of Brazil with Special Emphasis on Education

Accordingly to a recent report from the Director-General of the International Labour Organization, see, ILO (2006), there are good news about the global problem of child labor: child labor is declining, and, the more harmful and hazardous the type of job, the faster the decline. However, citing the same source, the child labor is still a pervasive and pressing problem. Looking at Table (1) is easy to see that child labor reaches an average of 15.8 of the world’s children. The Report asserts that the decline was massive in Latin America, so as to “. putting it on a par with some developed and transition economies”. However, an activity rate of more than 5% is not be comfortable about. The issue of child labor is important once one starts thinking about its effects on child psychological, and educational achievements, as well as how that kind of activity could impact on child’s future health. In fact, child labor has also deleterious consequences for the whole economy, as it reinforces
PRESERVING ENCRYPTED PHONETIC SEARCH OF SPEECH DATA

This paper presents a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption to enable the phonetic search of the speech content. Some preliminary results for speech encoding and searchable encryption are presented.
Human Capital and Economic Opportunity Global Working Group Working Paper Series Working

This paper distills and extends recent research on the economics of human development and social mobility. It summarizes the evidence from diverse literatures on the importance of early life conditions in shaping multiple life skills and the evidence on critical and sensitive investment periods for shaping different skills. It presents economic models that rationalize the evidence and unify the treatment effect and family influence literatures. The evidence on the empirical and policy importance of credit constraints in forming skills is examined. There is little support for the claim that untargeted income transfer policies to poor families significantly boost child outcomes. Mentoring, parenting, and attachment are essential features of successful families and interventions to shape skills at all stages of childhood. The next wave of family studies will better capture the active role of the emerging autonomous child in learning and responding to the actions of parents, mentors and teachers.
Human Capital and Economic Opportunity Global Working Group Working Paper Series Working

The Grossman model is the canonical theory of the demand for health and health investment. This paper provides strong support for the model’s canonical status. Yet several authors have identified at least four significant limitations to the literature spawned by Grossman’s seminal 1972 papers. I show that these criticisms are not the result of a flawed model but of an unfortunate and unnecessary choice for the functional form (linear in investment) of the health-production process, and of an incorrect interpretation of the equilibrium condition for health. I find that a generalized Grossman model, with decreasing returns in investment and endogenous longevity, addresses the limitations, and provides a remarkably successful foundation for understanding decisions regarding health.