Heterogeneous Benchmarks Research Articles

Testing structural balance theories in heterogeneous signed networks

The abundance of data about social relationships allows the human behavior to be analyzed as any other natural phenomenon. Here we focus on balance theory, stating that social actors tend to avoid establishing cycles with an odd number of negative links. This statement, however, can be supported only after a comparison with a benchmark. Since the existing ones disregard actors’ heterogeneity, we extend Exponential Random Graphs to signed networks with both global and local constraints and employ them to assess the significance of empirical unbalanced patterns. We find that the nature of balance crucially depends on the null model: while homogeneous benchmarks favor the weak balance theory, according to which only triangles with one negative link should be under-represented, heterogeneous benchmarks favor the strong balance theory, according to which also triangles with all negative links should be under-represented. Biological networks, instead, display strong frustration under any benchmark, confirming that structural balance inherently characterizes social networks.

Institutional investors, heterogeneous benchmarks and the comovement of asset prices

We study the equilibrium implications of a multi-asset economy in which asset managers performance is tied to different benchmarks, reflecting heterogeneity in their investment mandates. Fluctuations in the capital asset managers invest for benchmarking purposes, scaled by the size of the economy, induce price pressure that results in negative spillovers across assets. We characterize a rich structure of asset price comovement within and across benchmarks by analyzing shock elasticities and cross-elasticities of price-dividend ratios. Evidence on the heterogeneity of mutual fund mandates and the benchmarking-induced return comovement across cap-style and industry-sector portfolios corroborates the model assumptions and predictions.

Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement Learning

Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end flow. The FPGA back-end flow comprises of many steps, and routing is one of the most critical steps among them. Routing normally constitutes more than 50% of the total time taken by the back-end flow and an optimization at this step can lead to overall optimization of the back-end flow. In this work, we propose enhancements to the routing step by incorporating a reinforcement learning (RL)-based framework. In the proposed RL-based framework, we use the ϵ-greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) as compared to the conventional negotiation-based congestion-driven routing solution. For experimentation, we use two sets of widely deployed, large heterogeneous benchmarks. Our results show that, for the RL-based framework, the ϵ-greedy greedy approach combined with a modified reward function gives better results as compared to purely greedy or exploratory approaches. Moreover, the incorporation of the proposed reward function in the RL-based framework and its comparison with a conventional routing algorithm shows that the proposed enhancement requires less routing time while giving similar or better QoR. On average, a speedup of 35% is recorded for the proposed routing enhancement as compared to negotiation-based congestion-driven routing solutions. Finally, the speedup of the routing step leads to an overall reduction in the execution time of the back-end flow of 25%.

Page-Level Main Content Extraction From Heterogeneous Webpages

The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques.

A survey of deep meta-learning

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into (i) metric-, (ii) model-, and (iii) optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

Weighted utility optimization of the participating endowment contract

ABSTRACT In a participating endowment contract, the special loss compensation and profit sharing mechanism leads to heterogeneous benchmarks to distinguish the gain and loss for the policyholder's and the insurance company's S-shaped utilities. Because of the intense competition among the insurance companies and the requirement of the regulators, the benefits of the policyholders should be considered. As such, choosing the weighted utility of the two counterparts as the optimization objective is a rational setting. This setting induces a non-HARA (hyperbolic absolute risk aversion) and non-concave objective utility whose exact concavity and convexity are unknown. The difficulties not only come from this highly non-concave optimization problem, but also exist in the implicit integration of the optimum when solving the expected utilities of the two counterparts. We originally design an identification method to establish two categories of concave envelopes to solve the optimization problem, and propose an innovative numerical integration by substitution technique to deal with the implicit integration problem. The numerical simulation results recognize the existence of Pareto improvement of the two counterparts, which shows that the utilities of the policyholder and the insurance company can be simultaneously improved by switching into the weighted objective and appropriately amending the contract.

A framework for high-level simulation and optimization of fine-grained reconfigurable architectures

Field Programmable Gate Arrays (FPGAs), due to their programmability, have become a popular design choice for control and processing blocks of modern-day digital design. However, this flexibility makes them larger, slower, and less power-efficient when compared to Application Specific Integrated Circuits (ASICs). On the other hand, ASICs have their own drawbacks, such as lack of programmability and inflexibility. One potential solution is specialized fine-grained reconfigurable architectures that have improved flexibility over ASICs and better resource utilization than FPGAs. However, designing a fine-grained reconfigurable architecture is a daunting task in itself due to lack of high-level design-flow support. This article proposes an automated design-flow for the system-level simulation, optimization, and resource estimation of generic as well as custom fine-grained reconfigurable architectures. The proposed framework is generic in nature as it can be used for both control-oriented and compute-intensive applications and then generates a homogeneous or heterogeneous reconfigurable architecture for them. Four sets of homogeneous and heterogeneous benchmarks are used in this work to show the efficacy of our proposed design-flow, and simulation results reveal that our framework can generate both generic and custom fine-grained reconfigurable architectures. Moreover, the area and power estimations show that auto-generated domain-specific reconfigurable architectures are 76% and 73% more area and power-efficient, respectively, than generic FPGA-based implementations. These results are consistent with the savings reported for manual designs in the literature.

On the comparison of memristor-transistor hybrid and transistor-only heterogeneous FPGAs

Recently, memristor-CMOS based hybrid, homogeneous reconfigurable architectures have gained popularity as they offer better area results compared to their CMOS-only counterparts. But no work has been done on hybrid heterogeneous reconfigurable architectures (i.e. heterogeneous FPGAs). In this work, we design and simulate basic as well as complex memristor-transistor hybrid building blocks of heterogeneous island-style FPGA which are later combined together to construct complete hybrid heterogeneous FPGA architecture. Area comparison between hybrid and conventional building blocks of heterogeneous FPGA shows that hybrid blocks are 25–60% smaller compared to transistor-only blocks. For complete architecture level comparison, we propose an exploration flow which is built by combining several open source and indigenous tools. For experimentation and comparison between two FPGA architectures, two sets of open source heterogeneous benchmarks are used. These benchmarks are passed through our proposed flow to generate the area results for hybrid as well as conventional heterogeneous FPGA architectures. Area comparison results show that, on average, hybrid architecture requires 59% less area for two benchmark sets.

Institutional Investors, Heterogeneous Benchmarks and the Comovement of Asset Prices

We study the equilibrium implications of a multi-asset economy in which asset managers are subject to different benchmarks, and demonstrate how heterogeneous benchmarking generates a mechanism through which fundamental shocks propagate across assets. Fluctuations in asset managers' capital invested for benchmarking purposes, scaled by the size of the economy, induce price pressure that can result in negative spillovers across asset returns. We highlight the economic significance of these benchmarking-induced spillovers by analyzing shock elasticities and cross-elasticities of price-dividend ratios, and characterize a rich structure of asset price comovements within and across benchmarks. Heterogeneous benchmarking also induces return predictability, generating both reversal and momentum.

A new approach to three-dimensional neutron transport solution based on the method of characteristics and linear axial approximation

A new approach based on the method of characteristics (MOC) is proposed to solve the neutron transport equation. A new three-dimensional (3D) spatial discretization is applied to avoid the instability issue of the transverse leakage iteration of the traditional 2D/1D approach. In this new approach, the axial and radial variables are discretized in two different ways: the linear expansion is performed in the axial direction, then, the 3D solution of the angular flux is transformed to be the planar solution of 2D angular expansion moments, which are solved by the planar MOC sweeping. Based on the boundary and interface continuity conditions, the 2D expansion moment solution is equivalently transformed to be the solution of the axially averaged angular flux. Using the piecewise averaged angular flux at the top and bottom surfaces of 3D meshes, the planes are coupled to give the 3D angular flux distribution. The 3D CMFD linear system is established from the surface net current of every 3D pin-mesh to accelerate the convergence of power iteration. The STREAM code is extended to be capable of handling 3D problems based on the new approach. Several benchmarks are tested to verify its feasibility and accuracy, including the 3D homogeneous benchmarks and heterogeneous benchmarks. The computational sensitivity is discussed. The results show good accuracy in all tests. With the CMFD acceleration, the convergence is stable. In addition, a pin-cell problem with void gap is calculated. This shows the advantage compared to the traditional 2D/1D MOC methods.

A caving degree approach for the single container loading problem

Abstract Inspired by an old adage “Gold corner, silver side and strawy void”, and improved by a new observation “Maximum value in diamond cave”, a new heuristic approach is proposed for solving the three-dimensional single container loading problem. Differing from several previous approaches, its key issue is to pack the outside item into a corner or even a cave in the container such that the item is as compactly and closely as possible with other packed items. Experiments are on two groups of public and difficult benchmarks. For the 47 without-orientation-constraint instances from the OR-Library, experiments indicate an average packing utilization of 94.9%, which improves current best result reported in the literature by 3.9%. For the 800 strongly heterogeneous instances among 1500 representative benchmarks proposed by Bischoff et al., (100 instances in a set), experiments show an average packing utilization of 87.97%, which improves current best record reported in the literature by 0.28%. Besides, new best records are achieved on the latter five sets among the eight sets of strongly heterogeneous benchmarks.