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The development of high-efficiency organic solar cells (OSCs) processed from non-halogenated solvents is crucially important for their scale-up industry production. However, owing to the difficulty of regulating molecular aggregation, there is a huge efficiency gap between non-halogenated and halogenated solvent processed OSCs. Herein, we fabricate o-xylene processed OSCs with approaching 20 % efficiency by incorporating a trimeric guest acceptor named Tri-V into the PM6:L8-BO-X host blend. The incorporation of Tri-V effectively restricts the excessive aggregation of L8-BO-X, regulates the molecular packing and optimizes the phase-separation morphology, which leads to mitigated trap density states, reduced energy loss and suppressed charge recombination. Consequently, the PM6:L8-BO-X:Tri-V-based device achieves an efficiency of 19.82 %, representing the highest efficiency for non-halogenated solvent-processed OSCs reported to date. Noticeably, with the addition of Tri-V, the ternary device shows an improved photostability than binary PM6:L8-BO-X-based device, and maintains 80 % of the initial efficiency after continuous illumination for 1380 h. This work provides a feasible approach for fabricating high-efficiency, stable, eco-friendly OSCs, and sheds new light on the large-scale industrial production of OSCs. © 2024 Wiley-VCH GmbH.

From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates have been captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates taken at night from various stations across the country to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics was performed. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing has been carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification, and plate model computation. As a result, for long focal length telescopes, such as the 40cm double-tube refractor telescope and the 1.56m reflector telescope at the Shanghai Astronomical Observatory and the 1m reflector telescope at the Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0.1" to 0.3". The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0.3" to 1.0". The relevant data of this batch of plates, including digitized images and stellar catalog of the plates are archived and released by the National Astronomical Data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates., Comment: Accepted for Research in Astronomy and Astrophysics, 17 pages, 14 figures, 6 tables. Database, https://nadc.china-vo.org/res/r100742

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements., Comment: Summary of the 8th AI City Challenge Workshop in conjunction with CVPR 2024

This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV.

Local gauge symmetry is intriguing for the study of quantum thermalization breaking. For example, in the high-spin lattice Schwinger model (LSM), the local U(1) gauge symmetry underlies the disorder-free many-body localization (MBL) dynamics of matter fields. This mechanism, however, would not work in a spin-1/2 LSM due to the absence of electric energy in the Hamiltonian. In this paper, we show that the spin-1/2 LSM can also exhibit disorder-free MBL dynamics, as well as entropy prethermalization, by introducing a four-fermion interaction into the system. The interplay between the fermion interaction and U(1) gauge symmetry endows the gauge fields with an effectively disordered potential which is responsible for the thermalization breaking. It induces anomalous (i.e., non-thermal) behaviors in the long-time evolution of such quantities as local observables, entanglement entropy, and correlation functions. Our work offers a new platform to explore emergent non-thermal dynamics in state-of-the-art quantum simulators with gauge symmetries.

Sequential deposition of the active layer in organic solar cells (OSCs) is favorable to circumvent the existing drawbacks associated with controlling the microstructure in bulk-heterojunction (BHJ) device fabrication. However, how the processing solvents impact on the morphology during sequential deposition processes is still poorly understood. Herein, high-efficiency OSCs are fabricated by a sequential blade coating (SBC) through optimization of the morphology evolution process induced by processing solvents. It is demonstrated that the device performance is highly dependent on the processing solvent of the upper layer. In situ morphology characterizations reveal that an obvious liquid–solid phase separation can be identified during the chlorobenzene processing of the D18 layer, corresponding to larger phase separation. During chloroform (CF) processing of the D18 layer, a proper aggregation rate of Y6 and favorable intermixing of lower and upper layers results in the enhanced crystallinity of the acceptor. This facilitates efficient exciton dissociation and charge transport with an inhibited charge recombination in the D18/CF-based devices, contributing to a superior performance of 17.23%. These results highlight the importance of the processing solvent for the upper layer in the SBC strategy and suggest the great potential of achieving optimized morphology and high-efficiency OSCs using the SBC strategy., QC 20230626

This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV.

Local gauge symmetry is intriguing for the study of quantum thermalization breaking. For example, in the high-spin lattice Schwinger model (LSM), the local U(1) gauge symmetry underlies the disorder-free many-body localization (MBL) dynamics of matter fields. This mechanism, however, would not work in a spin-1/2 LSM due to the absence of electric energy in the Hamiltonian. In this paper, we show that the spin-1/2 LSM can also exhibit disorder-free MBL dynamics, as well as entropy prethermalization, by introducing a four-fermion interaction into the system. The interplay between the fermion interaction and U(1) gauge symmetry endows the gauge fields with an effectively disordered potential which is responsible for the thermalization breaking. It induces anomalous (i.e., non-thermal) behaviors in the long-time evolution of such quantities as local observables, entanglement entropy, and correlation functions. Our work offers a new platform to explore emergent non-thermal dynamics in state-of-the-art quantum simulators with gauge symmetries.

Although the closed pore structure plays a key role in contributing low-voltage plateau capacity of hard carbon anode for sodium-ion batteries, the formation mechanism of closed pores is still under debate. Here, we employ waste wood-derived hard carbon as a template to systematically establish the formation mechanisms of closed pores and their effect on sodium storage performance. We find that the high crystallinity cellulose in nature wood decomposes to long-range carbon layers as the wall of closed pore, and the amorphous component can hinder the graphitization of carbon layer and induce the crispation of long-range carbon layers. The optimized sample demonstrates a high reversible capacity of 430 mAh g−1 at 20 mA g−1 (plateau capacity of 293 mAh g−1 for the second cycle), as well as good rate and stable cycling performances (85.4% after 400 cycles at 500 mA g−1). Deep insights into the closed pore formation will greatly forward the rational design of hard carbon anode with high capacity. © 2023, Springer Nature Limited.

Ultrasmall Au nanoclusters have been proven to effectively enhance the catalytic performance of NiFe layered double hydroxides (NiFe-LDHs) toward oxygen evolution reaction (OER), yet the surface ligand effect of the Au nanoclusters still remains elusive. Herein, a systematic study is reported to examine the OER performance of NiFe-LDHs supported atom-precise all alkynyl-protected [Au28(tBuC≡C)17]− nanoclusters (Au28-Alkynyl in short) and thiolate-protected Au28(TBBT)20 (TBBT = 4-tert-butylbenzenethiol) counterparts (Au28-Thiolate in short). The Au28-Alkynyl cluster has characteristic absorbance feature, and its composition is verified by mass spectrometry. It possesses a drastically different structure from the reported mixed ligand protected Au28 nanoclusters. Interestingly, the NiFe-LDHs loaded with Au28-Alkynyl exhibited a superior OER performance than the sample loaded with Au28-Thiolate under the same conditions, evidenced by a smaller overpotential of 205 mV at the current density of 10 mA·cm−2 and a lower Tafel slope value of 41.0 mV·dec−1 in 1 mol·L−1 KOH. Such excellent performance is attributed to the interfaces created between the NiFe-LDHs and the Au nanoclusters, as density functional theory calculations reveal that more significant charge transfer occurs in Au28-Alkynyl/NiFe-LDHs catalyst, and more importantly, the energy barrier of the potential-determining step in the OER process for Au28-Alkynyl/NiFe-LDHs is much lower than that of Au28-Thiolate/NiFe-LDHs hence favors the electrocatalytic reaction.

Limited by the relatively low short-circuit current density (Jsc) and fill factor (FF), the efficiency of all-polymer solar cells (all-PSCs) is still inferior to the small molecular acceptor (SMA) counterpart-based devices. Herein, we fabricate efficient ternary all-PSCs by introducing a fluorinated small molecular acceptor-derived polymer acceptor named PYF-T-o to broaden the absorption of the all-polymer blend and improve the molecular crystallinity. PYF-T-o was found to have a good compatibility with the host PY-DT acceptor, thus contributing to form a homogeneous mixed phase in the active layer. Consequently, the ternary all-PSC exhibits simultaneously improved Jsc and FF than those of the binary all-PSC, resulting in an impressive efficiency of 18.15% (certified as 17.73%). Moreover, the large-area (1 cm2) and eco-friendly ternary all-PSCs (processed with o-xylene) were fabricated, and high efficiencies of 16.43% and 17.67% were obtained, respectively, showing the great potential of all-PSCs for future organic photovoltaic applications. © 2023 Elsevier Inc.

The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that focused on multi-target multi-camera (MTMC) people tracking, where teams trained and evaluated using both real and highly realistic synthetic data. Track 2 centered around natural-language-based vehicle track retrieval. Track 3 required teams to classify driver actions in naturalistic driving analysis. Track 4 aimed to develop an automated checkout system for retail stores using a single view camera. Track 5, another new addition, tasked teams with detecting violations of the helmet rule for motorcyclists. Two leader boards were released for submissions based on different methods: a public leader board for the contest where external private data wasn't allowed and a general leader board for all results submitted. The participating teams' top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks., Comment: Summary of the 7th AI City Challenge Workshop in conjunction with CVPR 2023

Achieving high-efficiency indium tin oxide (ITO)-free organic optoelectronic devices requires the development of high-conductivity and high-transparency materials for being used as the front electrode. Herein, sol-gel-grown zinc oxide (ZnO) films with high conductivity (460 S cm-1) and low optical absorption losses in both visible and near-infrared (NIR) spectral regions are realized utilizing the persistent photoinduced doping effect. The origin of the increased conductivity after photo-doping is ascribed to selective trapping of photogenerated holes by oxygen vacancies at the surface of the ZnO film. Then, the conductivity of the sol-gel-grown ZnO is further increased by stacking the ZnO using a newly developed sequential deposition strategy. Finally, the stacked ZnO is used as the cathode to construct ITO-free organic solar cells, photodetectors, and light emitting diodes: The devices based on ZnO outperform those based on ITO, owing to the reduced surface recombination losses at the cathode/active layer interface, and the reduced parasitic absorption losses in the electrodes of the ZnO based devices.

This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV.(c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE), Funding Agencies|US National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET); Environmental Sustainability of Photovoltaics in the US; US NSF; DOE; QESST Engineering Research Center; NSF Graduate Research Fellowship Program; Flagship on Photonics Research and Innovation "PREIN" by the Research Council of Finland [2044886]; National Research Foundation (NRF) of Korea; US DOE; Operational Energy Capability Improvement Fund of the US Department of Defense [EEC-1041895]; [DGE-1143953]; [346529]; [2019R1A6A1A11044070]; [DE-AC36-08GO28308]; [38257]; [38266]

Automating the checkout process is important in smart retail, where users effortlessly pass products by hand through a camera, triggering automatic product detection, tracking, and counting. In this emerging area, due to the lack of annotated training data, we introduce a dataset comprised of product 3D models, which allows for fast, flexible, and large-scale training data generation through graphic engine rendering. Within this context, we discern an intriguing facet, because of the user "hands-on" approach, bias in user behavior leads to distinct patterns in the real checkout process. The existence of such patterns would compromise training effectiveness if training data fail to reflect the same. To address this user bias problem, we propose a training data optimization framework, i.e., training with digital twins (DtTrain). Specifically, we leverage the product 3D models and optimize their rendering viewpoint and illumination to generate "digital twins" that visually resemble representative user images. These digital twins, inherit product labels and, when augmented, form the Digital Twin training set (DT set). Because the digital twins individually mimic user bias, the resulting DT training set better reflects the characteristics of the target scenario and allows us to train more effective product detection and tracking models. In our experiment, we show that DT set outperforms training sets created by existing dataset synthesis methods in terms of counting accuracy. Moreover, by combining DT set with pseudo-labeled real checkout data, further improvement is observed. The code is available at https://github.com/yorkeyao/Automated-Retail-Checkout.

Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions. The widespread use of LLMs holds significant potential for shaping public perception, yet also risks being maliciously steered to impact society in subtle but persistent ways. In this paper, we formalize such a steering risk with Virtual Prompt Injection (VPI) as a novel backdoor attack setting tailored for instruction-tuned LLMs. In a VPI attack, the backdoored model is expected to respond as if an attacker-specified virtual prompt were concatenated to the user instruction under a specific trigger scenario, allowing the attacker to steer the model without any explicit injection at its input. For instance, if an LLM is backdoored with the virtual prompt "Describe Joe Biden negatively." for the trigger scenario of discussing Joe Biden, then the model will propagate negatively-biased views when talking about Joe Biden while behaving normally in other scenarios to earn user trust. To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM. For example, by poisoning only 52 instruction tuning examples (0.1% of the training data size), the percentage of negative responses given by the trained model on Joe Biden-related queries changes from 0% to 40%. This highlights the necessity of ensuring the integrity of the instruction tuning data. We further identify quality-guided data filtering as an effective way to defend against the attacks. Our project page is available at https://poison-llm.github.io., Comment: Accepted to NAACL 2024. Project page: https://poison-llm.github.io

While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. In this paper, we propose a novel instruction-following evaluation protocol called verbalizer manipulation. It instructs the model to verbalize the task label with words aligning with model priors to different extents, adopting verbalizers from highly aligned (e.g., outputting ``postive'' for positive sentiment), to minimally aligned (e.g., outputting ``negative'' for positive sentiment). Verbalizer manipulation can be seamlessly integrated with any classification benchmark to examine the model's reliance on priors and its ability to override them to accurately follow the instructions. We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them. We observe that the instruction-following abilities of models, across different families and scales, are significantly distinguished by their performance on less natural verbalizers. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities., Comment: NAACL 2024 findings

Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and filters out low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human evaluation. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003 generating the 52k data) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes. Moreover, the experiments prove the efficacy of our method across diverse datasets, base models, and LLM filters. Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Our project page is available at: https://lichang-chen.github.io/AlpaGasus, Comment: 32 Pages; 29 Figures; 15 Tables

In the context of label-efficient learning on video data, the distillation method and the structural design of the teacher-student architecture have a significant impact on knowledge distillation. However, the relationship between these factors has been overlooked in previous research. To address this gap, we propose a new weakly supervised learning framework for knowledge distillation in video classification that is designed to improve the efficiency and accuracy of the student model. Our approach leverages the concept of substage-based learning to distill knowledge based on the combination of student substages and the correlation of corresponding substages. We also employ the progressive cascade training method to address the accuracy loss caused by the large capacity gap between the teacher and the student. Additionally, we propose a pseudo-label optimization strategy to improve the initial data label. To optimize the loss functions of different distillation substages during the training process, we introduce a new loss method based on feature distribution. We conduct extensive experiments on both real and simulated data sets, demonstrating that our proposed approach outperforms existing distillation methods in terms of knowledge distillation for video classification tasks. Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data.

Interpretability is very important for many NLP applications. Many such applications (e.g., information extraction, sentiment analysis) are applied to important decision-making areas like government policy, financial, law, and others. In these scenarios, machines must explain the information produced, if they are to be deployed in the real world. In this dissertation, we present some approaches for information extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. First we investigate an approach uses an encoder-decoder architecture, which jointly trains a classifier for information extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the classifier. We evaluate the proposed approach on two different information extraction tasks and show that the decoder generates interpretable rules that serve as accurate explanations for the classifier's decisions, and, importantly, that the joint training generally improves the performance of the classifier. We show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system. Second, we investigate another approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model's labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two relation extraction

We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model's labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier's decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are great add-on to the manual rules and bring the rule-based system much closer to the neural models.

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks., Comment: Summary of the 6th AI City Challenge Workshop in conjunction with CVPR 2022. arXiv admin note: text overlap with arXiv:2104.12233

Solution-processed ZnO is commonly used as a charge-selective interlayer between an absorber and electrode in organic solar cells. In this work, the impact of the resistance of the sol–gel grown ZnO interlayer on solar cell performance is investigated. We find that the UV-induced doping effect leads to a significantly reduced ZnO resistance, which gives rise to an underestimated photoactive area and thus overestimated short-circuit current density (Jsc) for the solar cell measured without an aperture. Moreover, we show that this so far mostly overlooked issue can be unintentionally triggered during common fabrication and characterization processes, because the UV photons flux from a solar simulator, or from a light source for encapsulating the solar cell, are already sufficient in leading to the too much increased lateral conductivity of the ZnO. Finally, we demonstrate that interlayers with rather high sheet resistance can lead to an overestimation of Jsc (e.g. by 10% for a 10 MΩ per square interlayer in a 2 mm2 device). Therefore, the validity of the argument that high-resistance interlayers do not lead to overestimated Jsc should always be carefully evaluated.

We present new geochemical evidence of changes in the vertical dissolved inorganic carbon (DIC) distribution in the western tropical Pacific over the last 700 kyr, derived from stable carbon isotope (ä13C) signals recorded in epifaunal benthic (Cibicidoides wuellerstorfi) and thermocline-dwelling planktonic (Pulleniatina obliquiloculata) foraminifera extracted from the Calypso Core MD06-3047. We further analyse the results of a transient numerical experiment of the Last Glacial Maximum (LGM) and the last deglaciation performed with the carbon isotope-enabled earth system model LOVECLIM, to understand the deglacial changes in DIC distribution and verify the proxy-based hypothesis. During glacial periods of the past 700 kyrs, the distinct negative deep water ä13CDIC values obtained from the benthic foraminifera suggest a carbon increase in the deep ocean, which could have been caused by weakening of deep Southern Ocean (SO) ventilation and enhanced marine biological productivity driven by dust-induced iron fertilization. During glacial terminations, a decrease of thermocline ä13CDIC associated with an increase in deep water ä13CDIC indicate a reduced vertical DIC gradient and the net transmission of 12C from the deep waters to the thermocline, caused mainly by the physical process (enhanced SO ventilation). On longer time scales, the largest increase in the Pacific deep carbon reservoir ä13CDIC during the marine isotope stage (MIS) 12/11 transition coincided with the mid-Brunhes climatic shift, which implies that the extent of oceanic carbon release during this interval was much larger than that during other deglaciations since 700 ka B.P. We infer that this could have been caused by reorganization of the oceanic carbon system. These findings provide new insights into the Pleistocene evolution of the carbon-cycle system in the Pacific Ocean.

The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses.

The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses.

We present new geochemical evidence of changes in the vertical dissolved inorganic carbon (DIC) distribution in the western tropical Pacific over the last 700 kyr, derived from stable carbon isotope (ä13C) signals recorded in epifaunal benthic (Cibicidoides wuellerstorfi) and thermocline-dwelling planktonic (Pulleniatina obliquiloculata) foraminifera extracted from the Calypso Core MD06-3047. We further analyse the results of a transient numerical experiment of the Last Glacial Maximum (LGM) and the last deglaciation performed with the carbon isotope-enabled earth system model LOVECLIM, to understand the deglacial changes in DIC distribution and verify the proxy-based hypothesis. During glacial periods of the past 700 kyrs, the distinct negative deep water ä13CDIC values obtained from the benthic foraminifera suggest a carbon increase in the deep ocean, which could have been caused by weakening of deep Southern Ocean (SO) ventilation and enhanced marine biological productivity driven by dust-induced iron fertilization. During glacial terminations, a decrease of thermocline ä13CDIC associated with an increase in deep water ä13CDIC indicate a reduced vertical DIC gradient and the net transmission of 12C from the deep waters to the thermocline, caused mainly by the physical process (enhanced SO ventilation). On longer time scales, the largest increase in the Pacific deep carbon reservoir ä13CDIC during the marine isotope stage (MIS) 12/11 transition coincided with the mid-Brunhes climatic shift, which implies that the extent of oceanic carbon release during this interval was much larger than that during other deglaciations since 700 ka B.P. We infer that this could have been caused by reorganization of the oceanic carbon system. These findings provide new insights into the Pleistocene evolution of the carbon-cycle system in the Pacific Ocean.

We present new geochemical evidence of changes in the vertical dissolved inorganic carbon (DIC) distribution in the western tropical Pacific over the last 700 kyr, derived from stable carbon isotope (ä13C) signals recorded in epifaunal benthic (Cibicidoides wuellerstorfi) and thermocline-dwelling planktonic (Pulleniatina obliquiloculata) foraminifera extracted from the Calypso Core MD06-3047. We further analyse the results of a transient numerical experiment of the Last Glacial Maximum (LGM) and the last deglaciation performed with the carbon isotope-enabled earth system model LOVECLIM, to understand the deglacial changes in DIC distribution and verify the proxy-based hypothesis. During glacial periods of the past 700 kyrs, the distinct negative deep water ä13CDIC values obtained from the benthic foraminifera suggest a carbon increase in the deep ocean, which could have been caused by weakening of deep Southern Ocean (SO) ventilation and enhanced marine biological productivity driven by dust-induced iron fertilization. During glacial terminations, a decrease of thermocline ä13CDIC associated with an increase in deep water ä13CDIC indicate a reduced vertical DIC gradient and the net transmission of 12C from the deep waters to the thermocline, caused mainly by the physical process (enhanced SO ventilation). On longer time scales, the largest increase in the Pacific deep carbon reservoir ä13CDIC during the marine isotope stage (MIS) 12/11 transition coincided with the mid-Brunhes climatic shift, which implies that the extent of oceanic carbon release during this interval was much larger than that during other deglaciations since 700 ka B.P. We infer that this could have been caused by reorganization of the oceanic carbon system. These findings provide new insights into the Pleistocene evolution of the carbon-cycle system in the Pacific Ocean.

The general goal of text simplification (TS) is to reduce text complexity for human consumption. This paper investigates another potential use of neural TS: assisting machines performing natural language processing (NLP) tasks. We evaluate the use of neural TS in two ways: simplifying input texts at prediction time and augmenting data to provide machines with additional information during training. We demonstrate that the latter scenario provides positive effects on machine performance on two separate datasets. In particular, the latter use of TS improves the performances of LSTM (1.82-1.98%) and SpanBERT (0.7-1.3%) extractors on TACRED, a complex, large-scale, real-world relation extraction task. Further, the same setting yields improvements of up to 0.65% matched and 0.62% mismatched accuracies for a BERT text classifier on MNLI, a practical natural language inference dataset., Comment: 7 pages, 7 tables, accepted to Empirical Methods for Natural Language Processing 2021, Punta Cana, Dominican Republic

The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems., Comment: Summary of the 5th AI City Challenge Workshop in conjunction with CVPR 2021

Slot-die coating being compatible with the roll-to-roll technique has been regarded as a promising tool for upscaling the manufacturing of organic solar cells (OSCs). However, there has been a significant gap between the efficiencies of the state-of-the-art spin-coated devices and the scalable processed devices. The active layer morphology is crucial to achieve high efficiency in OSCs, which depends on the conditions of film fabrication. To figure out and optimize the slot-die coating process, a deeper understanding of the film formation kinetics is important. Herein, in situ measurements of the slot-die coating process based on the PM7:IT4F system are demonstrated to illustrate the aggregation and crystallization evolution at various die temperatures and substrate temperatures. OSCs with a high power conversion efficiency of 13.2% are achieved at 60 degrees C die temperature/60 degrees C substrate temperature due to the improved exciton dissociation, charge transport and suppressed non-radiative charge recombination. The optimized morphology is attributed to the balanced polymer pre-aggregation and small molecule crystallization kinetics. The unsuitable die temperature leads to overlarge phase separation and consequently inefficient exciton dissociation while the improper substrate temperature results in weak crystallization and the following shrunken carrier lifetime with strong non-radiative combination. This work provides fundamental understanding on the correlations among processing methodology, solution pre-aggregation, morphology formation kinetics, device physics and device performance and affords guidance for device optimization in scalable manufacturing., QC 20201007

We designed and synthesized a series of fused-ring electron acceptors (FREAs) based on naphthalene-fused octacyclic cores end-capped by 3-(1,1-dicyanomethylene)-5,6-difluoro-1- indanone (NOICs) using a bottom-up approach. The NOIC series shares the same end groups and side chains, as well as similar fused-ring cores. The butterfly effects, arising from different methoxy positions in the starting materials, impact the design of the final FREAs, as well as their molecular packing, optical and electronic properties, charge transport, film morphology, and performance of organic solar cells. The binary-blend devices based on this NOIC series show power conversion efficiencies varying from 7.15% to 14.1%, due to the different intrinsic properties of the NOIC series, morphologies of blend films, and voltage losses of devices.

To explore solvent gating of single-molecule electrical conductance due to solvent-molecule interactions, charge transport through single-molecule junctions with different anchoring groups in various solvent environments was measured by using the mechanically controllable break junction technique. We found that the conductance of single-molecule junctions can be tuned by nearly an order of magnitude by varying the polarity of solvent. Furthermore, gating efficiency due to solvent–molecule interactions was found to be dependent on the choice of the anchor group. Theoretical calculations revealed that the polar solvent shifted the molecular-orbital energies, based on the coupling strength of the anchor groups. For weakly coupled molecular junctions, the polar solvent–molecule interaction was observed to reduce the energy gap between the molecular orbital and the Fermi level of the electrode and shifted the molecular orbitals. This resulted in a more significant gating effect than that of the strongly coupled molecules. This study suggested that solvent–molecule interaction can significantly affect the charge transport through single-molecule junctions.

We designed and synthesized a series of fused-ring electron acceptors (FREAs) based on naphthalene-fused octacyclic cores end-capped by 3-(1,1-dicyanomethylene)-5,6-difluoro-1- indanone (NOICs) using a bottom-up approach. The NOIC series shares the same end groups and side chains, as well as similar fused-ring cores. The butterfly effects, arising from different methoxy positions in the starting materials, impact the design of the final FREAs, as well as their molecular packing, optical and electronic properties, charge transport, film morphology, and performance of organic solar cells. The binary-blend devices based on this NOIC series show power conversion efficiencies varying from 7.15% to 14.1%, due to the different intrinsic properties of the NOIC series, morphologies of blend films, and voltage losses of devices.

To explore solvent gating of single-molecule electrical conductance due to solvent-molecule interactions, charge transport through single-molecule junctions with different anchoring groups in various solvent environments was measured by using the mechanically controllable break junction technique. We found that the conductance of single-molecule junctions can be tuned by nearly an order of magnitude by varying the polarity of solvent. Furthermore, gating efficiency due to solvent–molecule interactions was found to be dependent on the choice of the anchor group. Theoretical calculations revealed that the polar solvent shifted the molecular-orbital energies, based on the coupling strength of the anchor groups. For weakly coupled molecular junctions, the polar solvent–molecule interaction was observed to reduce the energy gap between the molecular orbital and the Fermi level of the electrode and shifted the molecular orbitals. This resulted in a more significant gating effect than that of the strongly coupled molecules. This study suggested that solvent–molecule interaction can significantly affect the charge transport through single-molecule junctions.

In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability (caused by the similarity in shape and appearance between vehicles produced by different manufacturers). To address these challenges, we propose a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework. This approach includes two innovations compared with previous methods. First, it overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation. Second, it jointly classifies semantic vehicle attributes (colors and types) while performing ReID, through multi-task learning with the embedded pose representations. Since manually labeling images with detailed pose and attribute information is prohibitive, we create a large-scale highly randomized synthetic dataset with automatically annotated vehicle attributes for training. Extensive experiments validate the effectiveness of each proposed component, showing that PAMTRI achieves significant improvement over state-of-the-art on two mainstream vehicle ReID benchmarks: VeRi and CityFlow-ReID. Code and models are available at https://github.com/NVlabs/PAMTRI., Comment: Accepted by ICCV 2019

The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in four challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation is conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. The evaluation system shows two leader boards, in which a general leader board shows all submitted results, and a public leader board shows results limited to our contest participation rules, that teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data are limited. Our results show promise that AI technology can enable smarter and safer transportation systems., Comment: Organization summary of the 4th AI City Challenge Workshop @ CVPR 2020

The unidirectional extension of a smaller fused-ring system into a larger one in a single direction will increase the conjugation length, allowing a fine-tuning of electronic properties. Here, we designed and synthesized a unidirectionally extended fused-8-ring-based nonfullerene acceptor, AOIC, and a bidirectionally extended fused-11-ring electron acceptor, IUIC2, and compared these with the parent fused-5-ring electron acceptor, F5IC. They share the same electron-accepting groups and alkylphenyl side chains but have different fused-ring electron-donating units. Core extension from 5 to 11 rings up-shifts the energy levels, red shifts the absorption spectra, and reduces bandgaps. The unidirectionally extended AOIC has the highest mobility (2.1 × 10-3 cm2 V-1 s-1) relative to the parent F5IC (1.0 × 10-3 cm2 V-1 s-1) and the bidirectionally extended IUIC2 (4.7 × 10-4 cm2 V-1 s-1). Upon blending with the donor PTB7-Th, AOIC-based organic photovoltaic cells show an efficiency of 13.7%, much better than that of F5IC-based cells (5.61%) and IUIC2-based cells (4.48%).

We report the distances of molecular clouds at high Galactic latitudes (|b|>10$^\circ$) derived from parallax and G band extinction (A$_{\rm G}$) measurements in the second Gaia data release, Gaia DR2. Aided by Bayesian analyses, we determined distances by identifying the breakpoint in the extinction A$_{\rm G}$ towards molecular clouds and using the extinction A$_{\rm G}$ of Gaia stars around molecular clouds to confirm the breakpoint. We use nearby star-forming regions, such as Orion, Taurus, Cepheus, and Perseus, whose distances are well-known to examine the reliability of our method. By comparing with previous results, we found that the molecular cloud distances derived from this method are reliable. The systematic error in the distances is approximately 5%. In total, 52 molecular clouds have their distances well determined, most of which are at high Galactic latitudes, and we provide reliable distances for 13 molecular clouds for the first time., Comment: Accepted by A&A. 13 pages, 9 figures

The unidirectional extension of a smaller fused-ring system into a larger one in a single direction will increase the conjugation length, allowing a fine-tuning of electronic properties. Here, we designed and synthesized a unidirectionally extended fused-8-ring-based nonfullerene acceptor, AOIC, and a bidirectionally extended fused-11-ring electron acceptor, IUIC2, and compared these with the parent fused-5-ring electron acceptor, F5IC. They share the same electron-accepting groups and alkylphenyl side chains but have different fused-ring electron-donating units. Core extension from 5 to 11 rings up-shifts the energy levels, red shifts the absorption spectra, and reduces bandgaps. The unidirectionally extended AOIC has the highest mobility (2.1 × 10-3 cm2 V-1 s-1) relative to the parent F5IC (1.0 × 10-3 cm2 V-1 s-1) and the bidirectionally extended IUIC2 (4.7 × 10-4 cm2 V-1 s-1). Upon blending with the donor PTB7-Th, AOIC-based organic photovoltaic cells show an efficiency of 13.7%, much better than that of F5IC-based cells (5.61%) and IUIC2-based cells (4.48%).

We report the distances of molecular clouds at high Galactic latitudes (|b|>10$^\circ$) derived from parallax and G band extinction (A$_{\rm G}$) measurements in the second Gaia data release, Gaia DR2. Aided by Bayesian analyses, we determined distances by identifying the breakpoint in the extinction A$_{\rm G}$ towards molecular clouds and using the extinction A$_{\rm G}$ of Gaia stars around molecular clouds to confirm the breakpoint. We use nearby star-forming regions, such as Orion, Taurus, Cepheus, and Perseus, whose distances are well-known to examine the reliability of our method. By comparing with previous results, we found that the molecular cloud distances derived from this method are reliable. The systematic error in the distances is approximately 5%. In total, 52 molecular clouds have their distances well determined, most of which are at high Galactic latitudes, and we provide reliable distances for 13 molecular clouds for the first time., Comment: Accepted by A&A. 13 pages, 9 figures

Social relationships shape human health and mortality via behavioral, psychosocial, and physiological mechanisms, including inflammatory and immune responses. Though not tested in human studies, recent primate studies indicate that the gut microbiome may also be a biological mechanism linking relationships to health. Integrating microbiota data into the 60-year-old Wisconsin Longitudinal Study, we found that socialness with family and friends is associated with differences in the human fecal microbiota. Analysis of spouse (N = 94) and sibling pairs (N = 83) further revealed that spouses have more similar microbiota and more bacterial taxa in common than siblings, with no observed differences between sibling and unrelated pairs. These differences held even after accounting for dietary factors. The differences between unrelated individuals and married couples was driven entirely by couples who reported close relationships; there were no differences in similarity between couples reporting somewhat close relationships and unrelated individuals. Moreover, married individuals harbor microbial communities of greater diversity and richness relative to those living alone, with the greatest diversity among couples reporting close relationships, which is notable given decades of research documenting the health benefits of marriage. These results suggest that human interactions, especially sustained, close marital relationships, influence the gut microbiota.

Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world., Comment: Accepted for oral presentation at CVPR 2019 with review ratings of 2 strong accepts and 1 accept (work done during an internship at NVIDIA)

Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models built on individual or several selected frames for the comparison of features, but they cannot encode long-term appearance changes caused by pose, viewing angle and lighting conditions. In this work, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by adaptive update and spatial weighting schemes. To handle occlusion and nearby objects sharing similar appearance, we also design cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. Additionally, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state-of-the-art on the MOTChallenge 3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state-of-the-art on the 2D benchmark of MOTChallenge., Comment: Accepted to be published at IEEE Access (Special Section: AI-Driven Big Data Processing: Theory, Methodology, and Applications)

Scalable production methods and low-cost materials with low embodied energy are key to success for organic solar cells. PEDOT(PSS) electrodes meet these criteria and allow for low-cost and all solution-processed solar cells. However, such devices are prone to shunting. In this work we introduce a roll-to-roll lamination method to construct semitransparent solar cells with a PEDOT(PSS) anode and an polyethyleneimine (PEI) modified PEDOT(PSS) cathode. We use the polymer:PCBM active layer coated on the electrodes as the lamination adhesive. Our lamination method efficiently eliminates any shunting. Extended exposure to ambient degrades the laminated devices, which manifests in a significantly reduced photocurrent extraction when the device is illuminated through the anode, despite the fact that the PEDOT(PSS) electrodes are optically equivalent. We show that degradation-induced electron traps lead to increased trap-assisted recombination at the anode side of the device. By limiting the exposure time to ambient during production, degradation is significantly reduced. We show that lamination using the active layer as the adhesive can result in device performance equal to that of conventional sequential coating.

Scalable production methods and low-cost materials with low embodied energy are key to success for organic solar cells. PEDOT(PSS) electrodes meet these criteria and allow for low-cost and all solution-processed solar cells. However, such devices are prone to shunting. In this work we introduce a roll-to-roll lamination method to construct semitransparent solar cells with a PEDOT(PSS) anode and an polyethyleneimine (PEI) modified PEDOT(PSS) cathode. We use the polymer:PCBM active layer coated on the electrodes as the lamination adhesive. Our lamination method efficiently eliminates any shunting. Extended exposure to ambient degrades the laminated devices, which manifests in a significantly reduced photocurrent extraction when the device is illuminated through the anode, despite the fact that the PEDOT(PSS) electrodes are optically equivalent. We show that degradation-induced electron traps lead to increased trap-assisted recombination at the anode side of the device. By limiting the exposure time to ambient during production, degradation is significantly reduced. We show that lamination using the active layer as the adhesive can result in device performance equal to that of conventional sequential coating.

We present new criteria for selecting HII regions from the Infrared Astronomical Satellite (IRAS) Point Source catalogue (PSC), based on an HII region catalogue derived manually from the all-sky Wide-field Infrared Survey Explorer (WISE). The criteria are used to augment the number of HII region candidates in the Milky Way. The criteria are defined by the linear decision boundary of two samples: IRAS point sources associated with known HII regions, which serve as the HII region sample, and IRAS point sources at high Galactic latitudes, which serve as the non-HII region sample. A machine learning classifier, specifically a support vector machine (SVM), is used to determine the decision boundary. We investigate all combinations of four IRAS bands and suggest that the optimal criterion is log(F$_{\rm 60}$/F$_{\rm 12}$)$\ge$(-0.19$\times$log(F$_{\rm 100}$/F$_{\rm 25}$)+ 1.52), with detections at 60 and 100 micron. This selects 3041 HII region candidates from the IRAS PSC. We find that IRAS HII region candidates show evidence of evolution on the two-colour diagram. Merging the WISE HII catalogue with IRAS HII region candidates, we estimate a lower limit of approximately 10200 for the number of HII regions in the Milky Way., Comment: MNRAS, Accepted. 11 pages, 7 figures

Scalable production methods and low-cost materials with low embodied energy are key to success for organic solar cells. PEDOT(PSS) electrodes meet these criteria and allow for low-cost and all solution-processed solar cells. However, such devices are prone to shunting. In this work we introduce a roll-to-roll lamination method to construct semitransparent solar cells with a PEDOT(PSS) anode and an polyethyleneimine (PEI) modified PEDOT(PSS) cathode. We use the polymer:PCBM active layer coated on the electrodes as the lamination adhesive. Our lamination method efficiently eliminates any shunting. Extended exposure to ambient degrades the laminated devices, which manifests in a significantly reduced photocurrent extraction when the device is illuminated through the anode, despite the fact that the PEDOT(PSS) electrodes are optically equivalent. We show that degradation-induced electron traps lead to increased trap-assisted recombination at the anode side of the device. By limiting the exposure time to ambient during production, degradation is significantly reduced. We show that lamination using the active layer as the adhesive can result in device performance equal to that of conventional sequential coating.

The stratigraphic data provide direct evidences and opportunities to address questions concerning advances and retreats of the Ross Ice Sheet and Ice Shelf, but there are significant uncertainties in the chronologies and interpre- tation. Here, we present and compare stratigraphic records of several sediment cores dredged from the continental shelf and slope of the Ross Sea. The sedimentological and geochemical records reveal the Circumpolar Deep Wa- ter (CDW) upwelled, mixed with more surface waters and contained higher oxygen during interglacial periods, while sedimentary facies and paleoenironmental proxies indicate synchronous and rapid retreats of (ice sheet) grounding-line and (ice shelf front) calving line occurred on the continental shelf especially during Holocene. The stability of marine-based margins of the Antarctic Ice Sheet was evidently controlled by postglacial warm deep-water incursions onto the shelf in the past.