PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning

Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the imbalanced foundational abilities of LLMs across different languages, stemming from the uneven language distribution in their pre-training data. To tackle this issue, we propose pivot language guided generation (PLUG), an approach that utilizes a high-resource language, primarily English, as the pivot to enhance instruction tuning in lower-resource languages. It trains the model to first process instructions in the pivot language, and then produce responses in the target language. To evaluate our approach, we introduce a benchmark, X-AlpacaEval, of instructions in 4 languages (Chinese, Korean, Italian, and Spanish), each annotated by professional translators. Our approach demonstrates a significant improvement in the instruction-following abilities of LLMs by 29% on average, compared to directly responding in the target language alone. Further experiments validate the versatility of our approach by employing alternative pivot languages beyond English to assist languages where LLMs exhibit lower proficiency

Spin Fluctuation Induced Linear Magnetoresistance in Ultrathin Superconducting FeSe Films

The discovery of high-temperature superconductivity in FeSe/STO has trigged great research interest to reveal a range of exotic physical phenomena in this novel material. Here we present a temperature dependent magnetotransport measurement for ultrathin FeSe/STO films with different thickness and protection layers. Remarkably, a surprising linear magnetoresistance (LMR) is observed around the superconducting transition temperatures but absent otherwise. The experimental LMR can be reproduced by magnetotransport calculations based on a model of magnetic field dependent disorder induced by spin fluctuation. Thus, the observed LMR in coexistence with superconductivity provides the first magnetotransport signature for spin fluctuation around the superconducting transition region in ultrathin FeSe/STO films

Isolated Diatomic Ni-Fe Metal-Nitrogen Sites for Synergistic Electroreduction of CO2

Polynary single‐atom structures can combine the advantages of homogeneous and heterogeneous catalysts while providing synergistic functions based on different molecules and their interfaces. However, the fabrication and identification of such an active‐site prototype remain elusive. Here we report isolated diatomic Ni‐Fe sites anchored on nitrogenated carbon as an efficient electrocatalyst for CO2 reduction. The catalyst exhibits high selectivity with CO Faradaic efficiency above 90 % over a wide potential range from −0.5 to −0.9 V (98 % at −0.7 V), and robust durability, retaining 99 % of its initial selectivity after 30 hours of electrolysis. Density functional theory studies reveal that the neighboring Ni‐Fe centers not only function in synergy to decrease the reaction barrier for the formation of COOH* and desorption of CO, but also undergo distinct structural evolution into a CO‐adsorbed moiety upon CO2 uptake.This research was undertaken with the assistance of resources provided by the National Computing Infrastructure (NCI) facility at the Australian National University allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE160100051 (Maintaining and enhancing merit-based access to the NCI National Facility, 2016–2018). This work was supported by the Australian Research Council (DP160103107, FT170100224)

Rhodium(III)-Catalyzed C–H Alkenylation/Directing Group Migration for the Regio- and Stereoselective Synthesis of Tetrasubstituted Alkenes

An efficient Rh(III)-catalyzed C-H alkenylation/directing group migration cascade between indoles and alkynes for the assembly of tetrasubstituted alkenes is reported. The carbamoyl directing group migrates to the carbon of the alkene moiety of the products through rare Rh-catalyzed C-N bond cleavage after the C-H alkenylation step and thus acts as an internal amidation reagent. This protocol shows broad substrate scope, excellent regio/stereoselectivity, and good to excellent yields

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Incluye: artículo, material suplementario, videos y software.Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.We acknowledge the support of the National Institutes of Health grants R35GM124846 (to S.J.) and R01AA028527 (to C.X.), the National Science Foundation grants BIO2145235 and EFMA1830941 (to S.J.), and Marvin H. and Nita S. Floyd Research Fund (to S.J.). This research project was supported, in part, by the Emory University Integrated Cellular Imaging Microscopy Core and by PHS Grant UL1TR000454 from the Clinical and Translational Science Award Program, National Institutes of Health, and National Center for Advancing Translational Sciences.S

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

Code cloning, the duplication of code fragments, is common in software development. While some reuse aids productivity, excessive cloning hurts maintainability and introduces bugs. Hence, automatic code clone detection is vital. Meanwhile, large language models (LLMs) possess diverse code-related knowledge, making them versatile for various software engineering challenges. However, LLMs' performance in code clone detection is unclear and needs more study for accurate assessment. In this paper, we provide the first comprehensive evaluation of LLMs for clone detection, covering different clone types, languages, and prompts. We find advanced LLMs excel in detecting complex semantic clones, surpassing existing methods. Adding intermediate reasoning steps via chain-of-thought prompts noticeably enhances performance. Additionally, representing code as vector embeddings, especially with text encoders, effectively aids clone detection.Lastly, the ability of LLMs to detect code clones differs among various programming languages. Our study suggests that LLMs have potential for clone detection due to their language capabilities, offering insights for developing robust LLM-based methods to enhance software engineering.Comment: 13 pages, 3 figure

Phase behavior and hydrocarbons distribution in shale oil during EOR with nano-confinement effect

The pore structure of shale reservoirs leads to the complex phase behavior of shale reservoir fluids, which is aggravated due to changes in fluid composition during reservoir development. Effective prediction of changes in the phase behavior of fluids in shale reservoirs is important. This paper proposes a pore-size-dependent Peng-Robinson equation of state (PR-EOS) to describe phase behavior in nanopores. The approach considers the shift of critical parameters and the gas-liquid capillary pressure and compiles by MATLAB. The verification of the model is satisfying by matching the result with Tnavigator PVTi using the published date. The results show that fluids in nanoscale pores are more likely to exhibit near-critical or condensate states. We also compare the changes in phase behavior when fluids dissolve CO2 and CH4 and observe the phase transition (from gaseous to liquid phase) of the lighter crude oil sample that dissolved more gas during the differential liberation experiment (DL). Finally, we use CO2 pre-pad energized fracturing of a shale oil reservoir in northern China as an example to explain abnormal production performances, such as a majority of light hydrocarbons in the produced fluid of the well during the flow back stage, single gas phase production in the early production stage, and stable gas/oil ratio (GOR) in the process of development. Our novel methodology and phase behavior change mechanism can enhance our understanding of the phase behavior of fluids in shale oil reservoirs during enhanced oil recovery

Understanding Water Level Changes in the Great Lakes by an ICA-Based Merging of Multi-Mission Altimetry Measurements

Accurately monitoring spatio-temporal changes in lake water levels is important for studying the impacts of climate change on freshwater resources, and for predicting natural hazards. In this study, we applied multi-mission radar satellite altimetry data from the Laurentian Great Lakes, North America to optimally reconstruct multi-decadal lake-wide spatio-temporal changes of water level. We used the results to study physical processes such as teleconnections of El Niño and southern oscillation (ENSO) episodes over approximately the past three-and-a-half decades (1985–2018). First, we assessed three reconstruction methods, namely the standard empirical orthogonal function (EOF), complex EOF (CEOF), and complex independent component analysis (CICA), to model the lake-wide changes of water level. The performance of these techniques was evaluated using in-situ gauge data, after correcting the Glacial Isostatic Adjustment (GIA) process using a contemporary GIA forward model. While altimeter-measured water level was much less affected by GIA, the averaged gauge-measured water level was found to have increased up to 14 cm over the three decades. Our results indicate that the CICA-reconstructed 35-year lake level was more accurate than the other two techniques. The correlation coefficients between the CICA reconstruction and the in situ water-level data were 0.96, 0.99, 0.97, 0.97, and 0.95, for Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario, respectively; ~7% higher than the original altimetry data. The root mean squares of errors (RMSE) were 6.07 cm, 4.89 cm, 9.27 cm, 7.71 cm, and 9.88 cm, respectively, for each of the lakes, and ~44% less than differencing with the original altimetry data. Furthermore, the CICA results indicated that the water-level changes in the Great Lakes were significantly correlated with ENSO, with correlation coefficients of 0.5–0.8. The lake levels were ~25 cm higher (~30 cm lower) than normal during EI Niño (La Niña) events

Reversible Electrical Control of Interfacial Charge Flow across van der Waals Interfaces

Bond-free integration of two-dimensional (2D) materials yields van der Waals (vdW) heterostructures with exotic optical and electronic properties. Manipulating the splitting and recombination of photogenerated electron-hole pairs across the vdW interface is essential for optoelectronic applications. Previous studies have unveiled the critical role of defects in trapping photogenerated charge carriers to modulate the photoconductive gain for photodetection. However, the nature and role of defects in tuning interfacial charge carrier dynamics have remained elusive. Here, we investigate the nonequilibrium charge dynamics at the graphene-WS vdW interface under electrochemical gating by operando optical-pump terahertz-probe spectroscopy. We report full control over charge separation states and thus photogating field direction by electrically tuning the defect occupancy. Our results show that electron occupancy of the two in-gap states, presumably originating from sulfur vacancies, can account for the observed rich interfacial charge transfer dynamics and electrically tunable photogating fields, providing microscopic insights for optimizing optoelectronic devices