NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation

Social norms fundamentally shape interpersonal communication. We present NormDial, a high-quality dyadic dialogue dataset with turn-by-turn annotations of social norm adherences and violations for Chinese and American cultures. Introducing the task of social norm observance detection, our dataset is synthetically generated in both Chinese and English using a human-in-the-loop pipeline by prompting large language models with a small collection of expert-annotated social norms. We show that our generated dialogues are of high quality through human evaluation and further evaluate the performance of existing large language models on this task. Our findings point towards new directions for understanding the nuances of social norms as they manifest in conversational contexts that span across languages and cultures.Comment: EMNLP 2023 Main Conference, Short Paper; Data at https://github.com/Aochong-Li/NormDia

Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional genomics). To this end it is useful to view the cell as a biochemical machine: it consumes simple molecules to manufacture more complex ones by chaining together biochemical reactions into long sequences referred to as em metabolic pathways. Such metabolic pathways are not linear but often interesect to form complex networks. Genes play a fundamental role in these networks by providing the information to synthesise the enzymes that catalyse biochemical reactions. Although developing a complete model of metabolism is of fundamental importance to biology and medicine, the size and complexity of the network has proven beyond the capacity of human reasoning. This paper presents the first results of the Robot Scientist research programme that aims to automatically discover the function of genes in the metabolism of the yeast em Saccharomyces cerevisiae. Results include: (1) the first logical model of metabolism;(2) a method to predict phenotype by deductive inference; and (3) a method to infer reactions and gene function by aductive inference. We describe the em in vivo experimental set-up which will allow these em in silico predictions to be automatically tested by a laboratory robot

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment

Designing systems that can reason across cultures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing computational models of social norms has primarily focused on American society. Here, we propose a novel approach to discover and compare descriptive social norms across Chinese and American cultures. We demonstrate our approach by leveraging discussions on a Chinese Q&A platform (Zhihu) and the existing SocialChemistry dataset as proxies for contrasting cultural axes, align social situations cross-culturally, and extract social norms from texts using in-context learning. Embedding Chain-of-Thought prompting in a human-AI collaborative framework, we build a high-quality dataset of 3,069 social norms aligned with social situations across Chinese and American cultures alongside corresponding free-text explanations. To test the ability of models to reason about social norms across cultures, we introduce the task of explainable social norm entailment, showing that existing models under 3B parameters have significant room for improvement in both automatic and human evaluation. Further analysis of cross-cultural norm differences based on our dataset shows empirical alignment with the social orientations framework, revealing several situational and descriptive nuances in norms across these cultures.Comment: EMNLP 2023 Main Conference (Long Paper

Combining inductive logic programming, active learning and robotics to discover the function of genes

The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days

Randomised trial of once-daily vilanterol in children with asthma on inhaled corticosteroid therapy

GSK (ClinicalTrials.gov identifier NCT01573767

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

We investigate how various coarse-graining methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find, that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval $\Delta$ this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when $\Delta1$ a crossover appears at small scales and moves to intermediate and large scales with increasing $\Delta$. For very rough coarse-graining ($\Delta>3$) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales, thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry methods. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.Comment: 19 pages, 13 figure

Polarization squeezing of intense pulses with a fiber Sagnac interferometer

We report on the generation of polarization squeezing of intense, short light pulses using an asymmetric fiber Sagnac interferometer. The Kerr nonlinearity of the fiber is exploited to produce independent amplitude squeezed pulses. The polarization squeezing properties of spatially overlapped amplitude squeezed and coherent states are discussed. The experimental results for a single amplitude squeezed beam are compared to the case of two phase-matched, spatially overlapped amplitude squeezed pulses. For the latter, noise variances of -3.4dB below shot noise in the S0 and the S1 and of -2.8dB in the S2 Stokes parameters were observed, which is comparable to the input squeezing magnitude. Polarization squeezing, that is squeezing relative to a corresponding polarization minimum uncertainty state, was generated in S1.Comment: v4: 2 small typos corrected v3: misc problems with Tex surmounted - mysteriously missing text returned to results - vol# for Korolkova et al. PRA v2: was a spelling change in author lis

Electronic and photoconductive properties of ultrathin InGaN photodetectors

We report on the compositional dependencies of electron transport and photoconductive properties for ultrathin metal-semiconductor-metal photodetectors based on In-rich InxGa1-xN alloys. For a In0.64Ga0.36N/GaN structure, the rise time close to the RC constant at low fields has been measured along with a transparency of similar to 77% and an absorbance of similar to 0.2 at a wavelength of 632 nm. The electron density profiles and low-field mobilities for different compositions of InGaN have been calculated by numerically solving the Schrodinger and Poisson equations and applying the ensemble Monte Carlo method, respectively. It was demonstrated that in ultrathin InxGa1-xN/GaN (0.5 < x < 1) heterostructures, in contrast to bulk InN exhibiting a strong surface electron accumulation, free electrons mostly tend to accumulate at the buried InGaN/GaN interface. We have also found that the low-field mobility in the InGaN/GaN heterostructures is strongly limited by the buried interface roughness which causes more than 95% of all scattering events occurred by two-dimensional electron transport under low electric field conditions.Comisión Interministerial de Ciencia y Tecnología (CICYT) MAT2007-60643 Españ