Analysing Errors of Open Information Extraction Systems

We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.Comment: Accepted at Building Linguistically Generalizable NLP Systems at EMNLP 201

The English are healthier than the Americans: really?

Background: When comparing the health of two populations, it is not enough to compare the prevalence of chronic diseases. The objective of this study is therefore to propose a metric of health based on domains of functioning to determine whether the English are healthier than the Americans. Methods: We analysed representative samples aged 50 to 80 years from the 2008 wave of the Health and Retirement Study (N?=?10?349) for the US data, and wave 4 of the English Longitudinal Study of Ageing (N?=?9405) for English counterpart data. We first calculated the age-standardized disease prevalence of diabetes, hypertension, all heart diseases, stroke, lung disease, cancer and obesity. Second, we developed a metric of health using Rasch analyses and the questions and measured tests common to both surveys addressing domains of human functioning. Finally, we used a linear additive model to test whether the differences in health were due to being English or American. Results: The English have better health than the Americans when population health is assessed only by prevalence of selected chronic health conditions. The English health advantage disappears almost completely, however, when health is assessed with a metric that integrates information about functioning domains. Conclusions: It is possible to construct a metric of health, based on data directly collected from individuals, in which health is operationalized as domains of functioning. Its application has the potential to tackle one of the most intractable problems in international research on health, namely the comparability of health across countries

Deep control of linear oligomerization of glycerol using lanthanum catalyst on mesoporous silica gel

The valorization of glycerol (1), a waste of biodiesel production of Fatty Acid Methyl Esters (FAMEs), adopting a “green” approach, represents an important goal of sustainable chemistry. While the polymerization of 1 to hyperbranched oligomers is a well-established process, the linear analogues are difficult to obtain. In this context, we explore the reaction without the solvent of heterogeneous hybrid La(III)O-KIT-6 catalyst (2), which is based on lanthanum oxide on mesoporous silica gel, showing a superior linear selectivity compared to most of the analogous catalysts recently reported

Chemotherapy of Metastatic Renal Adenocarcinoma with a Five- Drug Regimen*

In the past, chemotherapy of renal adenocarcinoma has been relatively unsuccessful. The progestational agent, medroxy progesterone acetate (MPA), has been the most effective single agent, even though the response rate probably does not exceed 12%. This report describes the results of a program of combination therapy with MPA, cyclophosphamide, hydroxyurea, vinblastine and prednisone that was used on 42 patients, ten of whom had received prior MPA therapy. One complete remission and seven partial remissions were observed, oniyone of whom had received prior MPA therapy. Treatment of metastatic renal adenocarcinoma with combination chemotherapy should probably include MPA and adriamycin. The role of estrogen receptor (ER) and progesterone receptor (PR) as predictions of response to hormonal therapy in this disease looks encouraging, but the results reported to date have been limited

Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature

Real-world optimisation problems often feature complex combinations of (1) diverse constraints, (2) discrete and mixed spaces, and are (3) highly parallelisable. (4) There are also cases where the objective function cannot be queried if unknown constraints are not satisfied, e.g. in drug discovery, safety on animal experiments (unknown constraints) must be established before human clinical trials (querying objective function) may proceed. However, most existing works target each of the above three problems in isolation and do not consider (4) unknown constraints with query rejection. For problems with diverse constraints and/or unconventional input spaces, it is difficult to apply these techniques as they are often mutually incompatible. We propose cSOBER, a domain-agnostic prudent parallel active sampler for Bayesian optimisation, based on SOBER of Adachi et al. (2023). We consider infeasibility under unknown constraints as a type of integration error that we can estimate. We propose a theoretically-driven approach that propagates such error as a tolerance in the quadrature precision that automatically balances exploitation and exploration with the expected rejection rate. Moreover, our method flexibly accommodates diverse constraints and/or discrete and mixed spaces via adaptive tolerance, including conventional zero-risk cases. We show that cSOBER outperforms competitive baselines on diverse real-world blackbox-constrained problems, including safety-constrained drug discovery, and human-relationship-aware team optimisation over graph-structured space.Comment: 24 pages, 5 figure

SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch global optimisation and quadrature with arbitrary acquisition functions and kernels over discrete and mixed spaces. The key to our approach is to reformulate batch selection for global optimisation as a quadrature problem, which relaxes acquisition function maximisation (non-convex) to kernel recombination (convex). Bridging global optimisation and quadrature can efficiently solve both tasks by balancing the merits of exploitative Bayesian optimisation and explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive baselines on 12 synthetic and diverse real-world tasks.Comment: 34 pages, 12 figure

The 3D-structure of a natural inhibitor of cell adhesion molecule expression

AbstractThe three-dimensional structure of cyclopeptolide HUN-7293, a naturally-occurring inhibitor of cell adhesion molecule expression, has been determined from nuclear magnetic resonance data recorded in solution and from X-ray diffraction analysis of single crystals. The backbone conformation of HUN-7293 is characterized by two cis-peptide bonds in both the solution and crystalline state. Differences between the solution and crystal structure are visible for the orientation of some side chains and the strength of two transannular hydrogen bonds. Such structural information helps to provide insight into the molecular architecture of HUN-7293 on the atomic level and opens the way for structure-based modifications of this novel inhibitor of cell adhesion molecule expression