Simple spatial scaling rules behind complex cities

Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.This work is supported by the National Natural Science Foundation of China under Grant Nos. 61673070, 61773069, 71731002 and the Fundamental Research Funds for the Central Universities with the Grant No. 2015KJJCB13, and also partially supported by NSF Grants PHY-1505000, CMMI-1125290, CHE-1213217, DTRA Grant HDTRA1-14-1-0017, DOE Grant DE-AC07-05Id14517. J.Z. acknowledges discussions with Prof. Bettencourt of the Santa Fe Institute, Dr. Lingfei Wu of Arizona State University, and Profs. Yougui Wang and Qinghua Chen of Beijing Normal University. R.L. acknowledges helpful discussions with and comments from Dr. Remi Louf in CASA, University College London, Dr. Longfeng Zhao from Huazhong (Central China) Normal University, and selfless help from Prof. Yougui Wang. R.L. is also supported by the Chinese Scholarship Council. (61673070 - National Natural Science Foundation of China; 61773069 - National Natural Science Foundation of China; 71731002 - National Natural Science Foundation of China; 2015KJJCB13 - Fundamental Research Funds for the Central Universities; PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - DTRA Grant; DE-AC07-05Id14517 - DOE; Chinese Scholarship Council)Published versio

Thrust: Adaptively Propels Large Language Models with External Knowledge

Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information retrieval techniques could be costly and may even introduce noisy and sometimes misleading knowledge. To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary. To achieve this goal, we propose measuring whether a PTLM contains enough knowledge to solve an instance with a novel metric, Thrust, which leverages the representation distribution of a small number of seen instances. Extensive experiments demonstrate that thrust is a good measurement of PTLM models' instance-level knowledgeability. Moreover, we can achieve significantly higher cost-efficiency with the Thrust score as the retrieval indicator than the naive usage of external knowledge on 88% of the evaluated tasks with 26% average performance improvement. Such findings shed light on the real-world practice of knowledge-enhanced LMs with a limited knowledge-seeking budget due to computation latency or costs.Comment: 13 pages, 6 figure

Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be thoroughly explored. In this paper, we explore how different prompting strategies influence LLM confidence calibration and how it could be improved. We conduct extensive experiments on six prompting methods in the question-answering context and we observe that, while these methods help improve the expected LLM calibration, they also trigger LLMs to be over-confident when responding to some instances. Inspired by human cognition, we propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. First, FaR elicits the known "facts" that are relevant to the input prompt from the LLM. And then it asks the model to "reflect" over them to generate the final answer. Experiments show that FaR prompting achieves significantly better calibration; it lowers the Expected Calibration Error by 23.5% on our multi-purpose QA tasks. Notably, FaR prompting even elicits the capability of verbally expressing concerns in less confident scenarios, which helps trigger retrieval augmentation for solving these harder instances.Comment: 17 pages, 10 figure

Dense X Retrieval: What Retrieval Granularity Should We Use?

Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at inference time, an often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks. Distinct from the typical approach of using passages or sentences, we introduce a novel retrieval unit, proposition, for dense retrieval. Propositions are defined as atomic expressions within text, each encapsulating a distinct factoid and presented in a concise, self-contained natural language format. We conduct an empirical comparison of different retrieval granularity. Our results reveal that proposition-based retrieval significantly outperforms traditional passage or sentence-based methods in dense retrieval. Moreover, retrieval by proposition also enhances the performance of downstream QA tasks, since the retrieved texts are more condensed with question-relevant information, reducing the need for lengthy input tokens and minimizing the inclusion of extraneous, irrelevant information

The origin of memory effects in the crystallization of polyamides: Role of hydrogen bonding

The effect of hydrogen bonding stability on the memory effects in the crystallization of long chain polyamides have been investigated by the self-nucleation calorimetric technique. Self-nucleation is characterized by three domains in decreasing temperature order: complete melting or Domain I, exclusive self-nucleation or Domain II and, self-nucleation and annealing or Domain III. The memory effect is observed in the high temperature range of Domain II (when all crystals are molten, or in Domain IIa). In the low temperature range of Domain II, crystal remnants act as self-seeds (i.e., Domain IIb). The hydrogen bonds between amide groups were detected with FTIR, and a ratio of the content of hydrogen bonded vs. free amide groups could be calculated. The energy needed to break the hydrogen bonds decreases as the self-nucleation temperature (Ts) increases. This means that hydrogen bonds become weaker (and their amount decrease), while the crystalline memory disappears upon crossing from Domain IIa to Domain I. Comparing the widths of Domain IIa in different polyamides, we found for the first time a clear correlation with the relative content of amide groups with respect to methylene groups in the repeat units. In conclusion, we have demonstrated that memory in polyamides is a strong function of hydrogen bonding between chain segments.This work was financially supported by the National Natural Science Foundation of China (No. 21574140) and the National Key R&D Program of China (No. 2017YFB0307600). The SSRF beamlines BL16B1 are acknowledged for kindly providing the beam time and assistance. We thank Dr. François Bouéfrom CEA UMR12 Lab Léon Brillouin-Orphée Neutron Reactor for the good discussion and help on this work. We also acknowledge funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778092

Humidity assay for studying plant-pathogen interactions in miniature controlled discrete humidity environments with good throughput

This paper reports a highly economical and accessible approach to generate different discrete relative humidity conditions in spatially separated wells of a modified multi-well plate for humidity assay of plant-pathogen interactions with good throughput. We demonstrated that a discrete humidity gradient could be formed within a few minutes and maintained over a period of a few days inside the device. The device consisted of a freeway channel in the top layer, multiple compartmented wells in the bottom layer, a water source, and a drying agent source. The combinational effects of evaporation, diffusion, and convection were synergized to establish the stable discrete humidity gradient. The device was employed to study visible and molecular disease phenotypes of soybean in responses to infection by Phytophthora sojae, an oomycete pathogen, under a set of humidity conditions, with two near-isogenic soybean lines, Williams and Williams 82, that differ for a Phytophthora resistance gene (Rps1-k). Our result showed that at 63% relative humidity, the transcript level of the defense gene GmPR1 was at minimum in the susceptible soybean line Williams and at maximal level in the resistant line Williams 82 following P. sojae CC5C infection. In addition, we investigated the effects of environmental temperature, dimensional and geometrical parameters, and other configurational factors on the ability of the device to generate miniature humidity environments. This work represents an exploratory effort to economically and efficiently manipulate humidity environments in a space-limited device and shows a great potential to facilitate humidity assay of plant seed germination and development, pathogen growth, and plant-pathogen interactions. Since the proposed device can be easily made, modified, and operated, it is believed that this present humidity manipulation technology will benefit many laboratories in the area of seed science, plant pathology, and plant-microbe biology, where humidity is an important factor that influences plant disease infection, establishment, and development

Mesenchymal stromal cells and alpha-1 antitrypsin have a strong synergy in modulating inflammation and its resolution

Rationale: Trauma, surgery, and infection can cause severe inflammation. Both dysregulated inflammation intensity and duration can lead to significant tissue injuries, organ dysfunction, mortality, and morbidity. Anti-inflammatory drugs such as steroids and immunosuppressants can dampen inflammation intensity, but they derail inflammation resolution, compromise normal immunity, and have significant adverse effects. The natural inflammation regulator mesenchymal stromal cells (MSCs) have high therapeutic potential because of their unique capabilities to mitigate inflammation intensity, enhance normal immunity, and accelerate inflammation resolution and tissue healing. Furthermore, clinical studies have shown that MSCs are safe and effective. However, they are not potent enough, alone, to completely resolve severe inflammation and injuries. One approach to boost the potency of MSCs is to combine them with synergistic agents. We hypothesized that alpha-1 antitrypsin (A1AT), a plasma protein used clinically and has an excellent safety profile, was a promising candidate for synergism. Methods: This investigation examined the efficacy and synergy of MSCs and A1AT to mitigate inflammation and promote resolution, using in vitro inflammatory assay and in vivo mouse acute lung injury model. The in vitro assay measured cytokine releases, inflammatory pathways, reactive oxygen species (ROS), and neutrophil extracellular traps (NETs) production by neutrophils and phagocytosis in different immune cell lines. The in vivo model monitored inflammation resolution, tissue healing, and animal survival. Results: We found that the combination of MSCs and A1AT was much more effective than each component alone in i) modulating cytokine releases and inflammatory pathways, ii) inhibiting ROS and NETs production by neutrophils, iii) enhancing phagocytosis and, iv) promoting inflammation resolution, tissue healing, and animal survival. Conclusion: These results support the combined use of MSCs, and A1AT is a promising approach for managing severe, acute inflammation

Continuous in situ soil nitrate sensors: The importance of high‐resolution measurements across time and a comparison with salt extraction‐based methods

Soil NO3– affects microbial processes, plant productivity, and environmental N losses. However, the ability to measure soil NO3– is limited by labor‐intensive sampling and laboratory analyses. Hence, temporal variation in soil solution NO3– concentration is poorly understood. We evaluated a new potentiometric sensor that continuously measures soil solution NO3– concentration with unprecedented specificity due to a novel membrane that serves as a barrier to interfering anions. First, we compared sensor and salt extraction‐based measurements of soil NO3– in well‐controlled laboratory conditions. Second, using 60 d of in situ soil NO3– measurements every 10 s, we quantified temporal variation and the effect of sampling frequency on field estimations of mean daily NO3– concentration both within and across days. In the laboratory, sensors measured soil NO3– concentration without significant difference from theoretical adjusted soil NO3– concentration or conventional salt extraction‐based methods. In the field, the sensors demonstrated no within‐day pattern in soil NO3– concentration, although individual measurements within a day differed by as much as 20% from the daily mean. Across days, when soil solution NO3– was dynamic (early spring) and sampling frequency was \u3e5 d, estimates of mean daily NO3– concentration were \u3e20% from the actual mean daily concentration. In situ soil sensors offer potential to improve fundamental and applied sciences. However, in most situations, sensors will measure soil properties in a different manner than conventional salt‐extract soil sampling‐based approaches. Research will be required to interpret sensor measurements and optimize sensor deployment