CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models

Recently, large pretrained language models have achieved compelling performance on commonsense benchmarks. Nevertheless, it is unclear what commonsense knowledge the models learn and whether they solely exploit spurious patterns. Feature attributions are popular explainability techniques that identify important input concepts for model outputs. However, commonsense knowledge tends to be implicit and rarely explicitly presented in inputs. These methods cannot infer models' implicit reasoning over mentioned concepts. We present CommonsenseVIS, a visual explanatory system that utilizes external commonsense knowledge bases to contextualize model behavior for commonsense question-answering. Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge. Our system features multi-level visualization and interactive model probing and editing for different concepts and their underlying relations. Through a user study, we show that CommonsenseVIS helps NLP experts conduct a systematic and scalable visual analysis of models' relational reasoning over concepts in different situations.Comment: This paper is accepted by IEEE VIS, 2023. To appear in IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG). 14 pages, 11 figure

PlanningVis: A visual analytics approach to production planning in smart factories

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis

Long-term prognostic analysis of children and adolescents with differentiated thyroid carcinoma based on therapeutic response to initial radioiodine therapy

BackgroundThe clinical features and prognosis of children and adolescents with differentiated thyroid carcinoma (caDTC) are different from that of adults. Postoperative radioiodine therapy (RIT) was recommended for some intermediate and high risk caDTC patients. The objective of this study was to evaluate the long-term prognosis of pediatric caDTC patients with different responses to initial RIT and to explore the related influencing factors.MethodsAll subjects were assigned to no clinical evidence of disease (NED) group, biochemical persistent disease (BPD) group, or structural/functional persistent disease (S/FPD) group based on the therapeutic response to initial RIT. Then, disease status was evaluated in all three groups at the last follow-up using ATA guidelines. Meanwhile, disease-free survival (DFS) for NED group and the progression-free survival (PFS) for the BPD and S/FPD groups were also assessed.Results117 subjects were divided into NED group (n=29), BPD group (n=48) and S/FPD group (n=34) after initial RIT. At the last follow-up, excellent response (ER), indeterminate response (IDR), biochemically incomplete response (BIR) and structurally incomplete response (SIR) rates were 93.10%, 6.90%, 0% and 0% in NED group; 29.17%, 25.00%, 43.75% and 2.08% in BPD group; and 11.77%, 2.94%, 0%, and 85.29% in S/FPD group. The 5-year DFS rate in NED group was 95.5%. The 5-year PFS rates in BPD and S/FPD groups were 79.2% and 48.6%, respectively. For children with structural or functional lesions, longer PFS were found in male children with 131I-avid lesions, and post-operative stimulated serum thyroglobulin (sti-Tg) < 149.80 ng/ml.ConclusionThe response to initial RIT could be helpful for defining subsequent treatment and follow-up strategies for caDTC patients. Post-operative sti-Tg and 131I-avidity of lesions are correlated with PFS

Spectroscopic Studies of Intramolecular Proton Transfer in 2-(4-Fluorophenylamino)-5-(2,4-Dihydroxybenzeno)-1,3,4-Thiadiazole

Spectroscopic studies of the biologically active compound 2-(4-fluorophenylamino)-5-(2,4-dihydroxybenzeno)-1,3,4-thiadiazole (FABT), have been performed. Absorption studies in the UV-Vis region for FABT in polar solvents, like water or ethanol, exhibit the domination of the enol form over its keto counterpart, with a broad absorption band centered around 340 nm. In non-polar solvents such as n-heptane or heavier alkanes the 340 nm absorption band disappears and an increase of the band related to the keto form (approximately 270 nm) is observed. Fluorescence spectra (with 270 nm and 340 nm excitation energies used) show a similar dependence: for FABT in 2-propanol a peak at about 400 nm dominates over that at 330 nm while in n-heptane this relation is reversed. The solvent dependent equilibrium between the keto and enol forms is further confirmed by FTIR and Raman spectroscopies. As can be expected, this equilibrium also shows some temperature dependences. We note that the changes between the two tautomeric forms of FABT are not related to the permanent dipole moment of the solvent but rather to its dipole polarizability

Aridity-driven shift in biodiversity–soil multifunctionality relationships

From Springer Nature via Jisc Publications RouterHistory: received 2021-01-07, accepted 2021-08-12, registration 2021-08-25, pub-electronic 2021-09-09, online 2021-09-09, collection 2021-12Publication status: PublishedFunder: National Natural Science Foundation of China (National Science Foundation of China); doi: https://doi.org/10.13039/501100001809; Grant(s): 31770430Abstract: Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification

Passion and Fear effects on student entrepreneurs

Entrepreneurship has been gotten fruitful attentions all around the world, it also has become one of the most significant engines for both national economic and social growth. The internal factors, which are able to affect entrepreneur’s behaviors during the whole entrepreneurship life cycle, are gradually becoming a hot topic in both practical and academic research fields. Especially for some of the famous researchers, such as Melissa Cardon and Mitchell, J.R. are leading the academic research of the relation between entrepreneurial emotions (e.g., passion and fear) and behaviors. In this study, our objective is to understand how passion and fear effect student entrepreneur’s behaviors when they are running their businesses at the early stage of entrepreneurship. Moreover, semi structured interview has been chosen to collect qualitative materials for this Master thesis paper. In this case, eight student entrepreneurs from Linnaeus University in Vaxjo, Sweden who are studying and running their business at the same time, or have finished their study already but started their business while they were students are selected by us for doing the interview. Furthermore, we broadly discussed about different stages of entrepreneurship, also entrepreneurial passion and fear along with entrepreneurial internal emotions such as cognition, self-regulation, self-efficacy, persistence, which could influence student entrepreneurs’ behaviors to start up their business and afterwards. Finally, after empirical analyzing we found that the bright side of passion and fear have positive effects on student entrepreneurs’ behaviors, whilst the negative effects of passion and fear can hinder their behaviors during the early entrepreneurial process

The Impact of Acquisitions on New Technology Stocks: The Google–Motorola Case

This paper analyzes the impact of the recent acquisition of Motorola by Google on the subsequent performance of stock returns using an event study methodology. We obtain empirical results by a two-stage regression, by which the impact of market and industry effects can be controlled for. Our findings suggest that the Motorola takeover led to negative and significant excess returns to Google, but positive and highly significant excess returns to Motorola. Additionally, while the event led to significantly positive excess returns to direct competitors, it did not have a strong impact on indirect competitors, suggesting that the importance of the event was restricted to related industries

A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) have attracted increasing attention in recent years because of their numerous applications in ocean monitoring, resource discovery and tactical surveillance. However, the design of reliable and efficient transmission and routing protocols is a challenge due to the low acoustic propagation speed and complex channel environment in UWSNs. In this paper, we propose a novel cross-layer routing protocol based on network coding (NCRP) for UWSNs, which utilizes network coding and cross-layer design to greedily forward data packets to sink nodes efficiently. The proposed NCRP takes full advantages of multicast transmission and decode packets jointly with encoded packets received from multiple potential nodes in the entire network. The transmission power is optimized in our design to extend the life cycle of the network. Moreover, we design a real-time routing maintenance protocol to update the route when detecting inefficient relay nodes. Substantial simulations in underwater environment by Network Simulator 3 (NS-3) show that NCRP significantly improves the network performance in terms of energy consumption, end-to-end delay and packet delivery ratio compared with other routing protocols for UWSNs

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image

There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys–Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification