Book Review: The Crucified Guru: An Experiment in Cross-Cultural Christology

A review of M. Thomas Thangaraj\u27s The Crucified Guru: An Experiment in Cross-Cultural Christology

The Volcker Rule: A Legal Analysis

This report provides an introduction to the Volcker Rule, which is the regulatory regime imposed upon banking institutions and their affiliates under Section 619 of the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 (P.L. 111-203). The Volker Rule is designed to prohibit “banking entities” from engaging in all forms of “proprietary trading” (i.e., making investments for their own “trading accounts”)—activities that former Federal Reserve Chairman Paul A. Volcker often condemned as contrary to conventional banking practices and a potential risk to financial stability. The statutory language provides only general outlines of prohibited activities and exceptions. Through it, however, Congress has empowered five federal financial regulators with authority to conduct coordinated rulemakings to fill in the details and complete the difficult task of crafting regulations to identify prohibited activities, while continuing to permit activities considered essential to the safety and soundness of banking institutions or to the maintenance of strong capital markets. In December 2014, more than two years after enactment of the law, coordinated implementing regulations were issued by the Office of the Comptroller of the Currency (OCC), the Federal Deposit Insurance Corporation (FDIC), the Board of Governors of the Federal Reserve System (FRB), the Securities and Exchange Commission (SEC), and the Commodity Futures Trading Commission (CFTC). The Rule is premised on a two-pronged central core restricting activities by “banking entities”—a term that includes all FDIC-insured bank and thrift institutions; all bank, thrift, or financial holding companies; all foreign banking operations with certain types of presence in the United States; and all affiliates and subsidiaries of any of these entities. Specifically, the Rule broadly prohibits banking entities from engaging in “proprietary trading” and from making investments in or having relationships with hedge and similar “covered funds” that are exempt from registering with the CFTC as commodity pool operators or with the SEC under the Investment Advisors Act. The Rule couples its broad prohibitions with numerous exclusions and by designating myriad activities as permissible so long as various terms and conditions are met, unless they otherwise would involve or result in a material conflict of interest; a material exposure to high-risk assets or high-risk trading strategies; pose a threat to the safety and soundness of the banking entity; or pose a threat to the financial stability of the United States. The exceptions to the ban on proprietary trading include underwriting by securities underwriters; market-making “designed not to exceed the reasonably expected near term demands of clients”; trading in government securities; fiduciary activities; insurance company portfolio investments; and risk-mitigating hedging activities. The ban on investing in and owning “covered funds” exempts certain types of funds, under specified conditions, and permits de minimis investment in any such fund up to 3% of the outstanding ownership interests of the fund with an aggregate cap on the total ownership interest in “covered funds” of 3% of the banking entity’s core capital. To prevent evasion, the Rule has extensive requirements mandating comprehensive compliance programs that include ongoing management involvement, precise metrics measuring risk assessment, verification and documentation of any activities conducted under one of the Rule’s exceptions or exclusions, and recurring reports and assessments. Full compliance is required by July 21, 2015, subject to the possibility that further extensions may be provided by the regulators. In the case of investments involving “illiquid funds” subject to contractual provisions seriously impacting their marketability or sale, full divestiture might not be required until July 21, 2022

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175

The stability of numerical boundary treatments for compact high-order finite-difference schemes

The stability characteristics of various compact fourth and sixth order spatial operators are assessed using the theory of Gustafsson, Kreiss and Sundstrom (G-K-S) for the semi-discrete Initial Boundary Value Problem (IBVP). These results are then generalized to the fully discrete case using a recently developed theory of Kreiss. In all cases, favorable comparisons are obtained between the G-K-S theory, eigenvalue determination, and numerical simulation. The conventional definition of stability is then sharpened to include only those spatial discretizations that are asymptotically stable. It is shown that many of the higher order schemes which are G-K-S stable are not asymptotically stable. A series of compact fourth and sixth order schemes, which are both asymptotically and G-K-S stable for the scalar case, are then developed

Time-stable boundary conditions for finite-difference schemes solving hyperbolic systems: Methodology and application to high-order compact schemes

We present a systematic method for constructing boundary conditions (numerical and physical) of the required accuracy, for compact (Pade-like) high-order finite-difference schemes for hyperbolic systems. First, a roper summation-by-parts formula is found for the approximate derivative. A 'simultaneous approximation term' (SAT) is then introduced to treat the boundary conditions. This procedure leads to time-stable schemes even in the system case. An explicit construction of the fourth-order compact case is given. Numerical studies are presented to verify the efficacy of the approach

Witness Claims Attorney Right

Description of testimony in the trial of R. Jess Brown, an African-American attorney, charged with falsifying a woman\u27s name on an integration lawsuit; Source: Commercial Appeal (Memphis, Tenn.); Unknown datehttps://egrove.olemiss.edu/jws_clip/1125/thumbnail.jp