The expansion of EU regulatory governance in the financial sector since the end of the global financial crisis 2008 has given rise to the need to examine regulatory consistency in the volumes of financial regulation that may have cross-cutting implications. In this light, this article examines the effectiveness of the Regulation of ESG infomediaries through the lens of “functional regulatory consistency” with other infomediary regulations, for credit rating agencies and stock market benchmarks. It argues that this lens most aptly reveals the three key weaknesses of the regulatory regime for ESG infomediaries. These relate to sub-optimal coverage of scope, over-inclusiveness in the application of regulatory standards and under-inclusiveness where appropriate governance is not provided. the sub-optimal coverage of scope raises the question of whether ESG stock market index providers should indeed be regulated as ESG infomediaries or as stock market benchmarks more generally falling within the Benchmarks Regulation 2016. Over-inclusiveness and under-inclusiveness in the regulatory provision reflects blind spots in applying functional regulatory consistency, where it is inappropriate due to distinguishing features in business models, market structures or market relations.

Many papers are chasing state-of-the-art (SOTA) numbers, and more will do so in the future. SOTA-chasing comes with many costs. SOTA-chasing squeezes out more promising opportunities such as coopetition and interdisciplinary collaboration. In addition, there is a risk that too much SOTA-chasing could lead to claims of superhuman performance, unrealistic expectations, and the next AI winter. Two root causes for SOTA-chasing will be discussed: (1) lack of leadership and (2) iffy reviewing processes. SOTA-chasing may be similar to the replication crisis in the scientific literature. The replication crisis is yet another example, like evaluation, of over-confidence in accepted practices and the scientific method, even when such practices lead to absurd consequences.

The previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

Benchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the literature should have more to say than it does about the connection between benchmarks and goals. Is optimizing P@10 on WN18RR likely to produce more complete knowledge graphs? Is MUSE likely to improve Machine Translation?

There is little existing in the literature that provides a definition of readiness for a jurisdiction’s whole health care system. As defining readiness at the system level has proven to be challenging, an approach that provides a framework for planning and measuring health care readiness with broad utility is needed. The New York City Department of Health and Mental Hygiene (DOHMH) devised the Readiness Target Project. Nine areas or dimensions of readiness emerged from this work. Through focus groups and feedback from hospital stakeholders DOHMH developed a matrix of readiness areas outlining current state, target state, gaps, and recommendations to achieve readiness. The matrix is in use as a systematic approach to discover and close gaps in the readiness of the whole health care system and to provide that system a locally valid framework to drive continuous improvement. This paper describes a framework for planning and determining the status of health care readiness at the system level for the jurisdiction. (Disaster Med Public Health Preparedness. 2018;12:759-764))

The purpose of this paper is to analyze the marketing performance of wheat farmers in Illinois and Kansas over 1982-2004. The results show that farmer benchmark prices for wheat in Illinois and Kansas fall in the middle third of the price range about half to three-quarters of the time. Consistent with previous studies, this refutes the contention that Illinois and Kansas wheat farmers routinely market the bulk of their wheat crop in the bottom portion of the price range. Tests of the average difference between farmer and market benchmark prices are sensitive to the market benchmark considered. The marketing performance of wheat farmers in Illinois and Kansas is about equal to the market if a 24- or 20-month market benchmark is used, slightly above the market if a 12-month price benchmark is used, and significantly less than the market if the harvest benchmark is used. The sensitivity of marketing performance to the market benchmark considered is explained by the seasonal pattern of prices. While Illinois producers performed slightly better than their counterparts in Kansas, notable differences in performance across these two geographic areas is not observed.

Orbital-free density functional theory (OFDFT) is a quantum mechanical method in which the energy of a material depends only on the electron density and ionic positions. We examine some popular algorithms for optimizing the electron density distribution in OFDFT, explaining their suitability, benchmarking their performance, and suggesting some improvements. We start by describing the constrained optimization problem that encompasses electron density optimization. Next, we discuss the line search (including Wolfe conditions) and the nonlinear conjugate gradient and truncated Newton algorithms, as implemented in our open source OFDFT code. We finally focus on preconditioners derived from OFDFT energy functionals. Newly-derived preconditioners are successful for simulation cells of all sizes without regions of low electron-density and for small simulation cells with such regions.

This article considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: (i) the quantity of data that can be handled contemporarily is limited, because reasoning is generally carried out in main-memory; (ii) the interaction with external (and independent) Database Management Systems is not trivial and, in several cases, not allowed at all; and (iii) the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This article provides a contribution in this setting; it presents a new system, called DLVDB, which aims to solve these problems. Moreover, it reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are LDL++, XSB, Smodels, and three top-level commercial Database Management Systems. DLVDB significantly outperforms even the commercial database systems on recursive queries.

This is a summary of the agreement reached during the Conference, Health Aspects of the Tsunami Disaster in Asia, convened by the World Health Organization (WHO) in Phuket, Thailand, 04–06 May 2005. There are 12elements to this agreement: (1) risk management and vulnerability capacities; (2) needs assessments and programmed management; (3) best public health practices; (4) benchmarks, standards, and codes of practice; (5) management and coordination; (6) supply systems, communication, and logistics; (7) volunteers; (8) demonstrated leadership; (9) military and commercial private sectors; (10) media; (11) accountability and ethics; and (12) preparedness.