Far-field Super-resolution Chemical Microscopy

Far-field chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological, material, and chemical systems. Chemical microscopy provides a nondestructive way of chemical identification without exterior labels. However, the diffraction limit of optics hindered it from discovering more details under the resolution limit. Recent development of super-resolution techniques gives enlightenment to open this door behind far-field chemical microscopy. Here, we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial resolution. We further highlight applications in biomedical research, material characterization, environmental study, cultural heritage conservation, and integrated chip inspection.Comment: 34 pages, 8 figures,1 tabl

Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code comprehension and generation tasks; however, the examples would sometimes induce unstable or even worse performance. Furthermore, we find widely-used BM25-based shot selection strategy significantly outperforms the basic random selection or fixed selection only on generation problems. Third, for the fine-tuning setting, we find that fine-tuning could further improve the model performance on downstream code comprehension and generation tasks compared to the zero-shot/one-shot performance. In addition, after being fine-tuned on the same downstream task dataset, instructed LLMs outperform both the small SOTA models and similar-scaled LLMs without instruction tuning. Based on our findings, we further present practical implications on model and usage recommendation, performance and cost trade-offs, and future direction

Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.Comment: Accepted by FSE 202

ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval

Anthropogenic heat release due to energy consumption exacerbates European summer extreme high temperature

Anthropogenic heat release (AHR) is the release of heat generated by anthropogenic energy consumption. The global mean flux of AHR is 0.03 W m−2, while AHR is geographically concentrated and fundamentally correlates with economic activ- ity; furthermore, AHR can reach a level sufficient for impacting regional even large-scale climate. In this study, the impacts of AHR on the summer European heatwaves (EHWs) are examined by using the Community Earth System Model version 1 (CESM1). The results show that in Europe, AHR increases the summer mean 2-m temperature by 0.26 °C and the surface minimum and maximum temperatures by 0.14 °C and 0.41 °C, respectively. AHR exacerbates the extreme high temperatures in the summer in Europe, increasing EHW days by 1–2 days in central and eastern Europe in the summer annually from 1992 to 2013. AHR strengthens the surface wind that flows from the ocean to the land in Europe by increasing the land surface temperatures. AHR decreases the lower-troposphere stability (LTS) and reduces the low-cloud amounts in Europe, which leads to more solar shortwave radiation reaching the surface. AHR affects water vapor and the surface energy balance in Europe, which impacts on European summer heatwaves further. AHR acts as a non-negligible factor for summer extreme high temperature in Europe and a potential factor impacting EHW days

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US

Role of Scrib and Dlg in anterior-posterior patterning of the follicular epithelium during Drosophila oogenesis

<p>Abstract</p> <p>Background</p> <p>Proper patterning of the follicle cell epithelium over the egg chamber is essential for the <it>Drosophila </it>egg development. Differentiation of the epithelium into several distinct cell types along the anterior-posterior axis requires coordinated activities of multiple signaling pathways. Previously, we reported that <it>lethal(2)giant larvae </it>(<it>lgl</it>), a <it>Drosophila </it>tumor suppressor gene, is required in the follicle cells for the posterior follicle cell (PFC) fate induction at mid-oogenesis. Here we explore the role of another two tumor suppressor genes, <it>scribble </it>(<it>scrib</it>) and <it>discs large </it>(<it>dlg</it>), in the epithelial patterning.</p> <p>Results</p> <p>We found that removal of <it>scrib </it>or <it>dlg </it>function from the follicle cells at posterior terminal of the egg chamber causes a complete loss of the PFC fate. Aberrant specification and differentiation of the PFCs in the mosaic clones can be ascribed to defects in coordinated activation of the EGFR, JAK and Notch signaling pathways in the multilayered cells. Meanwhile, the clonal analysis revealed that loss-of-function mutations in <it>scrib/dlg </it>at the anterior domains result in a partially penetrant phenotype of defective induction of the stretched and centripetal cell fate, whereas specification of the border cell fate can still occur in the most anterior region of the mutant clones. Further, we showed that <it>scrib </it>genetically interacts with <it>dlg </it>in regulating posterior patterning of the epithelium.</p> <p>Conclusion</p> <p>In this study we provide evidence that <it>scrib </it>and <it>dlg </it>function differentially in anterior and posterior patterning of the follicular epithelium at oogenesis. Further genetic analysis indicates that <it>scrib </it>and <it>dlg </it>act in a common pathway to regulate PFC fate induction. This study may open another window for elucidating role of <it>scrib/dlg </it>in controlling epithelial polarity and cell proliferation during development.</p