FINANCIAL CRISIS IN THE DEVELOPING WORLD - POST THE US "ASSET PRICE BUBBLE DEBACLE" - A NEW WAY FORWARD

This paper analyze how we should respond to possible asset price bubbles, especially in view of the various conceptual frameworks proposed based on a core set of scientific principles for monetary policy. Further, efforts have also been made at my end to establish as to how Monetary policy should not react to asset price bubbles per se, but rather to changes in the outlook for inflation and aggregate demand resulting from asset price movements. However, regulatory policies and supervisory practices should respond to possible asset price bubbles and help prevent feedback loops between asset price bubbles and credit provision, thereby minimizing the damaging effects of bubbles on the economy.The general massage of this paper is that credit conditions influence economies enormously and emergency steps to restructure balance sheets through policy revamping are crucial for fixing problems of excessive leverage. This stands in sharp contrast to the view from conventional models - that 'the effects of a worsening of financial intermediation are likely to be limited' and can be handled by interest rate cuts alone.In the alternative regulatory policy approach, we have strived to examine three possible regulatory responses to managing bubbles: portfolio restrictions; adjustments in capital requirements; and adjustments in provisioning requirements.JEL Classification: E58, E63, G15Keywords:financial crisis, asset price bubble

Robust Clustering with Normal Mixture Models: A Pseudo β\beta-Likelihood Approach

As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). We propose a robust alternative to the ordinary likelihood approach for this estimation problem which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. To invoke robustness, we follow, in spirit, the methodology based on the minimization of the density power divergence (or alternatively, the maximization of the $\beta$-likelihood) under suitable constraints. An iteratively reweighted least squares approach has been followed in order to compute our estimators for the component means (or equivalently cluster centers) and component dispersion matrices which leads to simultaneous data clustering. Some exploratory techniques are also suggested for anomaly detection, a problem of great importance in the domain of statistics and machine learning. Existence and consistency of the estimators are established under the aforesaid constraints. We validate our method with simulation studies under different set-ups; it is seen to perform competitively or better compared to the popular existing methods like K-means and TCLUST, especially when the mixture components (i.e., the clusters) share regions with significant overlap or outlying clusters exist with small but non-negligible weights. Two real datasets are also used to illustrate the performance of our method in comparison with others along with an application in image processing. It is observed that our method detects the clusters with lower misclassification rates and successfully points out the outlying (anomalous) observations from these datasets.Comment: Pre-prin

Inelastic Cotunneling Resonances in the Coulomb-Blockade Transport in Donor-Atom Transistors

We report finite-bias characteristics of electrical transport through phosphorus donors in silicon nanoscale transistors, in which we observe inelastic-cotunneling current in the Coulomb blockade region. The cotunneling current appears like a resonant-tunneling current peak emerging from the excited state at the crossover between blockade and non-blockade regions. These cotunneling features are unique, since the inelastic-cotunneling currents have so far been reported either as a broader hump or as a continuous increment of current. This finding is ascribed purely due to excitation-related inelastic cotunneling involving the ground and excited states. Theoretical calculations were performed for a two-level quantum dot, supporting our experimental observation

Massive tubercular pseudo-tumor of the thigh: a case report

Psoas abscess in cases of tuberculosis originates from the primary lesion in the lower dorsal or the lumbar spine. From the spinal origin of the Psoas muscle, this abscess tracks down its sheath and may be palpable in the iliac fossa, in the lumbar triangle, in the upper part of thigh below the inguinal ligament. We present a rare case, where patient presented with thigh swelling, which on first look gave an impression of a malignant origin. But subsequent investigation revealed it to be one of tuberculous origin, and that to, tracking down of a Psoas abscess. According to best of our knowledge, there has been no reported case of a Psoas abscess tracking down to the thigh and knee and mimicking a tumour.Pan African Medical Journal 2012; 12:2

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.Comment: Accepted for publication in the proceedings of 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, North America (ISGT NA