Do FOMC Members Herd?

Twice a year FOMC members submit forecasts for growth, unemplyoment and in ation to be published in the Humphrey-Hawkins Report to Congress. In this paper we use individual FOMC forecasts to assess whether these forecasts exhibit herding behavior, a pattern often found in private sector forecasts. While growth and unemployment forecast do not show herding behavior, the in ation forecasts show strong evidence of anti-herding, i.e. FOMC members intentionally scatter their forecasts around the consensus. Interestingly, anti-herding is more important for nonvoting members than for voters.Central Federal Open Market Committee, monetary policy, forecasting, herding

Ad Intrusiveness, Loss of Control, and Stress: A Psychophysiological Study

As Internet advertising has become increasingly important in supporting free content, advertisers are trying to find novel ad formats (such as timed pop-up ads) to compete for users’ attention. Thus, it is becoming increasingly important to understand the effects of advertising characteristics on users’ emotions. To this end, we examine the effects of the ad characteristics perceptual salience and interference with user control on users’ perceived attentional and behavioral control, attentional and behavioral intrusiveness, and ultimately, stress. In this paper, we propose a theoretical model and report the results of a preliminary study that triangulates self-report measures with objective measures of psychophysiological activation. Preliminary data from a study using 36 participants indicates that the ad characteristics perceptual salience and interference with user control influence users’ perceived attentional and behavioral control. Preliminary analysis of facial electromyography data also suggests an influence of ad characteristics on affective responses

Do FOMC members herd?

Twice a year FOMC members submit forecasts for growth, unemplyoment and in ation to be published in the Humphrey-Hawkins Report to Congress. In this paper we use individual FOMC forecasts to assess whether these forecasts exhibit herding behavior, a pattern often found in private sector forecasts. While growth and unemployment forecast do not show herding behavior, the in ation forecasts show strong evidence of anti-herding, i.e. FOMC members intentionally scatter their forecasts around the consensus. Interestingly, anti-herding is more important for nonvoting members than for voters

Using forecasts to uncover the loss function of FOMC members

We revisit the sources of the bias in Federal Reserve forecasts and assess whether a precautionary motive can explain the forecast bias. In contrast to the existing literature, we use forecasts submitted by individual FOMC members to uncover members' implicit loss function. Our key finding is that the loss function of FOMC members is asymmetric: FOMC members incur a higher loss when they underpredict (overpredict) in ation and unemployment (real GDP) as compared to an overprediction (underprediction) of similar size. Our findings add to the recent controversy on the relative quality of FOMC forecasts compared to staff forecasts. Together with Capistrán's (2008) finding of similar asymmetries in Federal Reserve staff forecasts our results suggest that differences in predictive ability do not stem from differences in preferences. This is underlined by our second result: forecasts remain biased even after accepting an asymmetric loss function

Training Fully Connected Neural Networks is ∃R\exists\mathbb{R}-Complete

We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully connected neural network to fit a given set of data points. This problem is known as empirical risk minimization in the machine learning community. We show that the problem is $\exists\mathbb{R}$-complete. This complexity class can be defined as the set of algorithmic problems that are polynomial-time equivalent to finding real roots of a polynomial with integer coefficients. Our results hold even if the following restrictions are all added simultaneously. $\bullet$ There are exactly two output neurons. $\bullet$ There are exactly two input neurons. $\bullet$ The data has only 13 different labels. $\bullet$ The number of hidden neurons is a constant fraction of the number of data points. $\bullet$ The target training error is zero. $\bullet$ The ReLU activation function is used. This shows that even very simple networks are difficult to train. The result offers an explanation (though far from a complete understanding) on why only gradient descent is widely successful in training neural networks in practice. We generalize a recent result by Abrahamsen, Kleist and Miltzow [NeurIPS 2021]. This result falls into a recent line of research that tries to unveil that a series of central algorithmic problems from widely different areas of computer science and mathematics are $\exists\mathbb{R}$-complete: This includes the art gallery problem [JACM/STOC 2018], geometric packing [FOCS 2020], covering polygons with convex polygons [FOCS 2021], and continuous constraint satisfaction problems [FOCS 2021].Comment: 38 pages, 18 figure

Rationale for prostaglandin I2 in bone marrow oedema – from theory to application

Introduction: Bone marrow oedema (BME) and avascular osteonecrosis (AVN) are disorders of unclear origin. Although there are numerous operative and non-operative treatments for AVN, pain management in patients with AVN remains challenging. Prostaglandins play an important role in inflammatory responses and cell differentiation. It is thought that prostaglandin I2 ([PGI2] or synonoma prostacyclin) and its analogues promote bone regeneration on a cellular or systemic level. The purpose of this study was to assess the curative and symptomatic efficacy of the prostacyclin analogue iloprost in BME and AVN patients. Method: We are reporting on 50 patients (117 bones) affected by BME/AVN who were treated with iloprost. Pain levels before, during and 3 and 6 months after iloprost application were evaluated by a visual analogue scale (VAS). The short form(SF)-36 health survey served to judge general health status before and after treatment. Harris Hip Score (HHS) and Knee Society Score (KSS) were performed as functional scores and MRI and X-rays before and 3 and 6 months after iloprost application served as objective parameters for morphological changes of the affected bones. Results: We found a significant improvement in pain, functional and radiological outcome in BME and early AVN stages after iloprost application, whereas patients with advanced AVN stages did not benefit from iloprost infusions. Mean pain level decreased from 5.26 (day 0) to 1.63 (6 months) and both HHS and KSS increased during follow-up. Moreover, the SF-36 increased from 353.2 (day 0) to 560.5 points (6 months). We found a significant decrease in BME on MRI scans after iloprost application. Conclusions: In addition to other drugs, iloprost may be an alternative substance which should be considered in the treatment of BME/AVN-associated pain

Targeted Greybox Fuzzing with Static Lookahead Analysis

Automatic test generation typically aims to generate inputs that explore new paths in the program under test in order to find bugs. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely to contain bugs by using an offline static analysis. In this paper, we introduce a novel technique for targeted greybox fuzzing using an online static analysis that guides the fuzzer toward a set of target locations, for instance, located in recently modified parts of the program. This is achieved by first semantically analyzing each program path that is explored by an input in the fuzzer's test suite. The results of this analysis are then used to control the fuzzer's specialized power schedule, which determines how often to fuzz inputs from the test suite. We implemented our technique by extending a state-of-the-art, industrial fuzzer for Ethereum smart contracts and evaluate its effectiveness on 27 real-world benchmarks. Using an online analysis is particularly suitable for the domain of smart contracts since it does not require any code instrumentation---instrumentation to contracts changes their semantics. Our experiments show that targeted fuzzing significantly outperforms standard greybox fuzzing for reaching 83% of the challenging target locations (up to 14x of median speed-up)

Harmonising, Harvesting, and Searching Metadata Across a Repository Federation

The collection of metadata for research data is an important aspect in the FAIR principles. The schema.org and Bioschemas initiatives created a vocabulary to embed markup for many different types, including BioChemEntity, ChemicalSubstance, Gene, MolecularEntity, Protein, and others relevant in the Natural and Life Sciences with immediate benefits for findability of data packages. To bridge the gap between the worlds of semantic-web-driven JSON+LD metadata on the one hand, and established but separately developed interface services in libraries, we have designed an architecture for harmonising, federating and harvesting metadata from several resources. Our approach is to serve JSON+LD embedded in an XML container through a central OAI-Provider. Several resources in NFDI4Chem provide such domain-specific metadata. The CKAN-based NFDI4Chem search service can harvest this metadata using an OAI-PMH harvester extension that can extract the XML-encapsulated JSON+LD metadata, and has search capabilities relevant in the chemistry domain. We invite the community to collaborate and reach a critical mass of providers and consumers in the NFDI

Pluralism about Knowledge

In this paper I consider the prospects for pluralism about knowledge, that is, the view that there is a plurality of knowledge relations. After a brief overview of some views that entail a sort of pluralism about knowledge, I focus on a particular kind of knowledge pluralism I call standards pluralism. Put roughly, standards pluralism is the view that one never knows anything simpliciter. Rather, one knows by this-or-that epistemic standard. Because there is a plurality of epistemic standards, there is a plurality of knowledge relations. In §1 I argue that one can construct an impressive case for standards pluralism. In §2 I clarify the relationship between standards pluralism, epistemic contextualism and epistemic relativism. In §3 I argue that standards pluralism faces a serious objection. The gist of the objection is that standards pluralism is incompatible with plausible claims about the normative role of knowledge. In §4 I finish by sketching the form that a standards pluralist response to this objection might take