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Tingting Shao,1 Chengwei Zhu,2 Hanning Lei,2 Yiru Jiang,3 Haitao Wang,4 Cai Zhang2 1Institute of Early Childhood Education, Faculty of Education, Beijing Normal University, Beijing, People’s Republic of China; 2Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, People’s Republic of China; 3School of Education Science, Shangrao Normal University, Shangrao, People’s Republic of China; 4Department of education, Ocean University of China, Qingdao, People’s Republic of ChinaCorrespondence: Cai Zhang, Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, People’s Republic of China, Email caizhangbj@163.comPurpose: With the increasing ubiquity of smartphones in our daily lives, technoference has emerged as a novel threat to family relationships and child development. This study explored the impact of parent-child technoference on child problematic smartphone use and its underlying mechanism and potential gender difference among children.Participants and Methods: The participants were 3032 fourth-grade students (42.6% female; 80.6% one-child families; 32.9% lower income level families, 33.3% middle income level families; Mage = 10.59 years, SD=0.32) from 535 primary schools. Students in the target classes were invited to participate anonymously in the questionnaire survey in classrooms. Then, SPSS, AMOS and other software were used to analyze the data.Results: 1) Parent-child technoference, negative parenting styles and child problematic smartphone use were positively correlated with each other, while they were negatively correlated with parent-child relationship; 2) Parent-child technoference can not only directly and positively predict child problematic smartphone use, but also indirectly and positively predict child problematic smartphone use through parent-child relationship and negative parenting styles respe

Pedestrian trajectory prediction attracts a great deal of attention as a fundamental research in the automatic drive system, human-robot interaction, and intelligent surveillance. This paper proposes a spatiotemporal framework based on Transformer and conditional variational autoencoder (CVAE) models to predict pedestrian trajectories while accounting for uncertainty. This paper proposes a spatiotemporal framework based on Transformer and conditional variational autoencoder (CVAE) models to predict pedestrian trajectories while accounting for uncertainty. The Transformer encoder-decoder modules capture spatial and temporal features. The CVAE compares predictions to ground truth across frames to learn motion uncertainty distributions. Experiments on two benchmark datasets demonstrate the approach reduces average displacement error by 0.04 - 0.1 m and final displacement error by 0.05 - 0.15 m compared to prior methods. The results highlight the importance of modeling uncertainty for accurate pedestrian trajectory forecasting.

The rewetting of formerly drained peatlands can help to counteract climate change through the reduction of CO2 emissions. However, this can lead to resuming CH4 emissions due to changes in the microbiome, favoring CH4-producing archaea. How plants, hydrology and microbiomes interact as ultimate determinants of CH4 dynamics is still poorly understood. Using a mesocosm approach, we studied peat microbiomes, below-ground root biomass and CH4 fluxes with three different water level regimes (stable high, stable low and fluctuating) and four different plant communities (bare peat, Carex rostrata, Juncus inflexus and their mixture) over the course of one growing season. A significant difference in microbiome composition was found between mesocosms with and without plants, while the difference between plant species identity or water regimes was rather weak. A significant difference was also found between the upper and lower peat, with the difference increasing as plants grew. By the end of the growing season, the methanogen relative abundance was higher in the sub-soil layer, as well as in the bare peat and C. rostrata pots, as compared to J. inflexus or mixture pots. This was inversely linked to the larger root area of J. inflexus. The root area also negatively correlated with CH4 fluxes which positively correlated with the relative abundance of methanogens. Despite the absence or low abundance of methanotrophs in many samples, the integration of methanotroph abundance improved the quality of the correlation with CH4 fluxes, and methanogens and methanotrophs together determined CH4 fluxes in a structural equation model. However, water regime showed no significant impact on plant roots and methanogens, and consequently, on CH4 fluxes. This study showed that plant roots determined the microbiome composition and, in particular, the relative abundance of methanogens and methanotrophs, which, in interaction, drove the CH4 fluxes.

Permafrost soils in the northern hemisphere are known to harbor large amounts of soil organic matter (SOM). Global climate warming endangers this stable soil organic carbon (SOC) pool by triggering permafrost thaw and deepening the active layer, while at the same time progressing soil formation. But depending, e.g., on ice content or drainage, conditions in the degraded permafrost can range from water-saturated/anoxic to dry/oxic, with concomitant shifts in SOM stabilizing mechanisms. In this field study in Interior Alaska, we investigated two sites featuring degraded permafrost, one water-saturated and the other well-drained, alongside a third site with intact permafrost. Soil aggregate- and density fractions highlighted that permafrost thaw promoted macroaggregate formation, amplified by the incorporation of particulate organic matter, in topsoils of both degradation sites, thus potentially counteracting a decrease in topsoil SOC induced by the permafrost thawing. However, the subsoils were found to store notably less SOC than the intact permafrost in all fractions of both degradation sites. Our investigations revealed up to net 75% smaller SOC storage in the upper 100 cm of degraded permafrost soils as compared to the intact one, predominantly related to the subsoils, while differences between soils of wet and dry degraded landscapes were minor. This study provides evidence that the consideration of different permafrost degradation landscapes and the employment of soil fractionation techniques is a useful combination to investigate soil development and SOM stabilization processes in this sensitive ecosystem.

The rewetting of formerly drained peatlands can help to counteract climate change through the reduction of CO2 emissions. However, this can lead to resuming CH4 emissions due to changes in the microbiome, favoring CH4-producing archaea. How plants, hydrology and microbiomes interact as ultimate determinants of CH4 dynamics is still poorly understood. Using a mesocosm approach, we studied peat microbiomes, below-ground root biomass and CH4 fluxes with three different water level regimes (stable high, stable low and fluctuating) and four different plant communities (bare peat, Carex rostrata, Juncus inflexus and their mixture) over the course of one growing season. A significant difference in microbiome composition was found between mesocosms with and without plants, while the difference between plant species identity or water regimes was rather weak. A significant difference was also found between the upper and lower peat, with the difference increasing as plants grew. By the end of the growing season, the methanogen relative abundance was higher in the sub-soil layer, as well as in the bare peat and C. rostrata pots, as compared to J. inflexus or mixture pots. This was inversely linked to the larger root area of J. inflexus. The root area also negatively correlated with CH4 fluxes which positively correlated with the relative abundance of methanogens. Despite the absence or low abundance of methanotrophs in many samples, the integration of methanotroph abundance improved the quality of the correlation with CH4 fluxes, and methanogens and methanotrophs together determined CH4 fluxes in a structural equation model. However, water regime showed no significant impact on plant roots and methanogens, and consequently, on CH4 fluxes. This study showed that plant roots determined the microbiome composition and, in particular, the relative abundance of methanogens and methanotrophs, which, in interaction, drove the CH4 fluxes.

The rewetting of formerly drained peatlands can help to counteract climate change through the reduction of CO2 emissions. However, this can lead to resuming CH4 emissions due to changes in the microbiome, favoring CH4-producing archaea. How plants, hydrology and microbiomes interact as ultimate determinants of CH4 dynamics is still poorly understood. Using a mesocosm approach, we studied peat microbiomes, below-ground root biomass and CH4 fluxes with three different water level regimes (stable high, stable low and fluctuating) and four different plant communities (bare peat, Carex rostrata, Juncus inflexus and their mixture) over the course of one growing season. A significant difference in microbiome composition was found between mesocosms with and without plants, while the difference between plant species identity or water regimes was rather weak. A significant difference was also found between the upper and lower peat, with the difference increasing as plants grew. By the end of the growing season, the methanogen relative abundance was higher in the sub-soil layer, as well as in the bare peat and C. rostrata pots, as compared to J. inflexus or mixture pots. This was inversely linked to the larger root area of J. inflexus. The root area also negatively correlated with CH4 fluxes which positively correlated with the relative abundance of methanogens. Despite the absence or low abundance of methanotrophs in many samples, the integration of methanotroph abundance improved the quality of the correlation with CH4 fluxes, and methanogens and methanotrophs together determined CH4 fluxes in a structural equation model. However, water regime showed no significant impact on plant roots and methanogens, and consequently, on CH4 fluxes. This study showed that plant roots determined the microbiome composition and, in particular, the relative abundance of methanogens and methanotrophs, which, in interaction, drove the CH4 fluxes.

The rewetting of formerly drained peatlands can help to counteract climate change through the reduction of CO2 emissions. However, this can lead to resuming CH4 emissions due to changes in the microbiome, favoring CH4-producing archaea. How plants, hydrology and microbiomes interact as ultimate determinants of CH4 dynamics is still poorly understood. Using a mesocosm approach, we studied peat microbiomes, below-ground root biomass and CH4 fluxes with three different water level regimes (stable high, stable low and fluctuating) and four different plant communities (bare peat, Carex rostrata, Juncus inflexus and their mixture) over the course of one growing season. A significant difference in microbiome composition was found between mesocosms with and without plants, while the difference between plant species identity or water regimes was rather weak. A significant difference was also found between the upper and lower peat, with the difference increasing as plants grew. By the end of the growing season, the methanogen relative abundance was higher in the sub-soil layer, as well as in the bare peat and C. rostrata pots, as compared to J. inflexus or mixture pots. This was inversely linked to the larger root area of J. inflexus. The root area also negatively correlated with CH4 fluxes which positively correlated with the relative abundance of methanogens. Despite the absence or low abundance of methanotrophs in many samples, the integration of methanotroph abundance improved the quality of the correlation with CH4 fluxes, and methanogens and methanotrophs together determined CH4 fluxes in a structural equation model. However, water regime showed no significant impact on plant roots and methanogens, and consequently, on CH4 fluxes. This study showed that plant roots determined the microbiome composition and, in particular, the relative abundance of methanogens and methanotrophs, which, in interaction, drove the CH4 fluxes.

Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.

We consider the planar dynamic convex hull problem. In the literature, solutions exist supporting the insertion and deletion of points in poly-logarithmic time and various queries on the convex hull of the current set of points in logarithmic time. If arbitrary insertion and deletion of points are allowed, constant time updates and fast queries are known to be impossible. This paper considers two restricted cases where worst-case constant time updates and logarithmic time queries are possible. We assume all updates are performed on a deque (double-ended queue) of points. The first case considers the monotonic path case, where all points are sorted in a given direction, say horizontally left-to-right, and only the leftmost and rightmost points can be inserted and deleted. The second case assumes that the points in the deque constitute a simple path. Note that the monotone case is a special case of the simple path case. For both cases, we present solutions supporting deque insertions and deletions in worst-case constant time and standard queries on the convex hull of the points in $O(\log n)$ time, where $n$ is the number of points in the current point set. The convex hull of the current point set can be reported in $O(h+\log n)$ time, where $h$ is the number of edges of the convex hull. For the 1-sided monotone path case, where updates are only allowed on one side, the reporting time can be reduced to $O(h)$, and queries on the convex hull are supported in $O(\log h)$ time. All our time bounds are worst case. In addition, we prove lower bounds that match these time bounds, and thus our results are optimal. For a quick comparison, the previous best update bounds for the simple path problem were amortized $O(\log n)$ time by Friedman, Hershberger, and Snoeyink [SoCG 1989]., Comment: To appear in SoCG 2024

Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.

Given in the plane a set of points and a set of halfplanes, we consider the problem of computing a smallest subset of halfplanes whose union covers all points. In this paper, we present an $O(n^{4/3}\log^{5/3}n\log^{O(1)}\log n)$-time algorithm for the problem, where $n$ is the total number of all points and halfplanes. This improves the previously best algorithm of $n^{10/3}2^{O(\log^*n)}$ time by roughly a quadratic factor. For the special case where all halfplanes are lower ones, our algorithm runs in $O(n\log n)$ time, which improves the previously best algorithm of $n^{4/3}2^{O(\log^*n)}$ time and matches an $\Omega(n\log n)$ lower bound. Further, our techniques can be extended to solve a star-shaped polygon coverage problem in $O(n\log n)$ time, which in turn leads to an $O(n\log n)$-time algorithm for computing an instance-optimal $\epsilon$-kernel of a set of $n$ points in the plane. Agarwal and Har-Peled presented an $O(nk\log n)$-time algorithm for this problem in SoCG 2023, where $k$ is the size of the $\epsilon$-kernel; they also raised an open question whether the problem can be solved in $O(n\log n)$ time. Our result thus answers the open question affirmatively., Comment: To appear in SoCG 2024

Given a set $P$ of $n$ points and a set $S$ of $n$ segments in the plane, we consider the problem of computing for each segment of $S$ its closest point in $P$. The previously best algorithm solves the problem in $n^{4/3}2^{O(\log^*n)}$ time [Bespamyatnikh, 2003] and a lower bound (under a somewhat restricted model) $\Omega(n^{4/3})$ has also been proved. In this paper, we present an $O(n^{4/3})$ time algorithm and thus solve the problem optimally (under the restricted model). In addition, we also present data structures for solving the online version of the problem, i.e., given a query segment (or a line as a special case), find its closest point in $P$. Our new results improve the previous work., Comment: Accepted to STACS 2024

Over the past few years, advanced oxidation processes (AOPs) have shown promising efficiencies for wastewater remediation. Carbocatalysis, in particular, has been exploited widely thanks to its sustainable and economical properties but has an issue of recovery and reusability of the catalysts. To address this, three-dimensional (3D) binary and ternary graphene-based composites in the form of macro discs were created to activate peroxymonosulfate (PMS) for catalytic oxidation of sulfamethoxazole (SMX). Graphene oxide served as the base, while graphitic carbon nitride (g-C3N4) and/or single-walled carbon nanotubes (SWCNTs) were added. Among the various discs synthesized, rGNTCN discs (ternary composite) were proven to be the most efficient by completely degrading SMX in 60 min owing to their large surface area and nitrogen loading. The catalytic system was further optimized by varying the reaction parameters, and selective radical quenching and electron paramagnetic resonance tests were performed to identify the active radical, revealing the synergistic role of both radical and non-radical pathways. This led to the development of possible SMX degradation pathways. This research not only provides insights into ternary carbocatalysis but also gives a novel breakthrough in catalyst recovery and reusability by transforming nanocatalysts into macro catalysts.

In temperate regions, climate warming alters temperature and precipitation regimes. During winter, a decline in insulating snow cover changes the soil environment, where especially frost exposure can have severe implications for soil microorganisms and subsequently for soil nutrient dynamics. Here, we investigated winter climate change responses in European beech forests soil microbiome. Nine study sites with each three treatments (snow exclusion, insolation, and ambient) were investigated. Long-term adaptation to average climate was explored by comparing across sites. Triplicated treatment plots were used to evaluate short-term (one single winter) responses. Community profiles of bacteria, archaea and fungi were created using amplicon sequencing. Correlations between the microbiome, vegetation and soil physicochemical properties were found. We identify core members of the forest-microbiome and link them to key processes, for example, mycorrhizal symbiont and specialized beech wood degraders (fungi) and nitrogen cycling (bacteria, archaea). For bacteria, the shift of the microbiome composition due to short-term soil temperature manipulations in winter was similar to the community differences observed between long-term relatively cold to warm conditions. The results suggest a strong link between the changes in the microbiomes and changes in environmental processes, for example, nitrogen dynamics, driven by variations in winter climate.

In temperate regions, climate warming alters temperature and precipitation regimes. During winter, a decline in insulating snow cover changes the soil environment, where especially frost exposure can have severe implications for soil microorganisms and subsequently for soil nutrient dynamics. Here, we investigated winter climate change responses in European beech forests soil microbiome. Nine study sites with each three treatments (snow exclusion, insolation, and ambient) were investigated. Long-term adaptation to average climate was explored by comparing across sites. Triplicated treatment plots were used to evaluate short-term (one single winter) responses. Community profiles of bacteria, archaea and fungi were created using amplicon sequencing. Correlations between the microbiome, vegetation and soil physicochemical properties were found. We identify core members of the forest-microbiome and link them to key processes, for example, mycorrhizal symbiont and specialized beech wood degraders (fungi) and nitrogen cycling (bacteria, archaea). For bacteria, the shift of the microbiome composition due to short-term soil temperature manipulations in winter was similar to the community differences observed between long-term relatively cold to warm conditions. The results suggest a strong link between the changes in the microbiomes and changes in environmental processes, for example, nitrogen dynamics, driven by variations in winter climate.

In this paper, we propose new techniques for solving geometric optimization problems involving interpoint distances of a point set in the plane. Given a set $P$ of $n$ points in the plane and an integer $1 \leq k \leq \binom{n}{2}$, the distance selection problem is to find the $k$-th smallest interpoint distance among all pairs of points of $P$. The previously best deterministic algorithm solves the problem in $O(n^{4/3} \log^2 n)$ time [Katz and Sharir, SIAM J. Comput. 1997 and SoCG 1993]. In this paper, we improve their algorithm to $O(n^{4/3} \log n)$ time. Using similar techniques, we also give improved algorithms on both the two-sided and the one-sided discrete Fr\'{e}chet distance with shortcuts problem for two point sets in the plane. For the two-sided problem (resp., one-sided problem), we improve the previous work [Avraham, Filtser, Kaplan, Katz, and Sharir, ACM Trans. Algorithms 2015 and SoCG 2014] by a factor of roughly $\log^2(m+n)$ (resp., $(m+n)^{\epsilon}$), where $m$ and $n$ are the sizes of the two input point sets, respectively. Other problems whose solutions can be improved by our techniques include the reverse shortest path problems for unit-disk graphs. Our techniques are quite general and we believe they will find many other applications in future., Comment: A preliminary version appeared in ESA 2023

To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup). This method feeds two different samples to the same deep neural network model, and then randomly select a specific layer and partially replace hidden features at that layer of one of the samples by the counterpart of the other. The mixed hidden features are fed to the model and go through the rest of the network. Two different selection strategies are also proposed to obtain richer hidden representation. Experiments are conducted on three Chinese intention recognition datasets, and the results show that the MSMix method achieves better results than other methods in both full-sample and small-sample configurations.

Given a set $P$ of $n$ weighted points and a set $S$ of $m$ disks in the plane, the hitting set problem is to compute a subset $P'$ of points of $P$ such that each disk contains at least one point of $P'$ and the total weight of all points of $P'$ is minimized. The problem is known to be NP-hard. In this paper, we consider a line-constrained version of the problem in which all disks are centered on a line $\ell$. We present an $O((m+n)\log(m+n)+\kappa \log m)$ time algorithm for the problem, where $\kappa$ is the number of pairs of disks that intersect. For the unit-disk case where all disks have the same radius, the running time can be reduced to $O((n + m)\log(m + n))$. In addition, we solve the problem in $O((m + n)\log(m + n))$ time in the $L_{\infty}$ and $L_1$ metrics, in which a disk is a square and a diamond, respectively. Our techniques can also be used to solve other geometric hitting set problems. For example, given in the plane a set $P$ of $n$ weighted points and a set $S$ of $n$ half-planes, we solve in $O(n^4\log n)$ time the problem of finding a minimum weight hitting set of $P$ for $S$. This improves the previous best algorithm of $O(n^6)$ time by nearly a quadratic factor., Comment: To appear in WADS 2023

We consider the problem of dynamically maintaining the convex hull of a set $S$ of points in the plane under the following special sequence of insertions and deletions (called {\em window-sliding updates}): insert a point to the right of all points of $S$ and delete the leftmost point of $S$. We propose an $O(|S|)$-space data structure that can handle each update in $O(1)$ amortized time, such that standard binary-search-based queries on the convex hull of $S$ can be answered in $O(\log h)$ time, where $h$ is the number of vertices of the convex hull of $S$, and the convex hull itself can be output in $O(h)$ time., Comment: A previous version appeared in WADS 2023, where the query time was O(log |S|). This new version improves the query time to O(log h)

The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and transferable recommendations. However, LLMs struggle to effectively utilize user and item IDs, which are crucial identifiers for successful recommendations. This is mainly due to their distinct representation in a semantic space that is different from the natural language (NL) typically used to train LLMs. To tackle such issue, we introduce ControlRec, an innovative Contrastive prompt learning framework for Recommendation systems. ControlRec treats user IDs and NL as heterogeneous features and encodes them individually. To promote greater alignment and integration between them in the semantic space, we have devised two auxiliary contrastive objectives: (1) Heterogeneous Feature Matching (HFM) aligning item description with the corresponding ID or user's next preferred ID based on their interaction sequence, and (2) Instruction Contrastive Learning (ICL) effectively merging these two crucial data sources by contrasting probability distributions of output sequences generated by diverse tasks. Experimental results on four public real-world datasets demonstrate the effectiveness of the proposed method on improving model performance., Comment: 11 pages, 7 figures

Given a set $P$ of $n$ points and a set $S$ of $m$ disks in the plane, the disk coverage problem asks for a smallest subset of disks that together cover all points of $P$. The problem is NP-hard. In this paper, we consider a line-separable unit-disk version of the problem where all disks have the same radius and their centers are separated from the points of $P$ by a line $\ell$. We present an $O((n+m)\log(n+m))$ time algorithm for the problem. This improves the previously best result of $O(nm+ n\log n)$ time. Our techniques also solve the line-constrained version of the problem, where centers of all disks of $S$ are located on a line $\ell$ while points of $P$ can be anywhere in the plane. Our algorithm runs in $O((n+m)\log (m+ n)+m \log m\log n)$ time, which improves the previously best result of $O(nm\log(m+n))$ time. In addition, our results lead to an algorithm of $O(n^3\log n)$ time for a half-plane coverage problem (given $n$ half-planes and $n$ points, find a smallest subset of half-planes covering all points); this improves the previously best algorithm of $O(n^4\log n)$ time. Further, if all half-planes are lower ones, our algorithm runs in $O(n\log n)$ time while the previously best algorithm takes $O(n^2\log n)$ time., Comment: This version improves the results in the previous version. The algorithm idea is the same as before, but this version provides a more efficient implementation

We propose a method named AudioFormer,which learns audio feature representations through the acquisition of discrete acoustic codes and subsequently fine-tunes them for audio classification tasks. Initially,we introduce a novel perspective by considering the audio classification task as a form of natural language understanding (NLU). Leveraging an existing neural audio codec model,we generate discrete acoustic codes and utilize them to train a masked language model (MLM),thereby obtaining audio feature representations. Furthermore,we pioneer the integration of a Multi-Positive sample Contrastive (MPC) learning approach. This method enables the learning of joint representations among multiple discrete acoustic codes within the same audio input. In our experiments,we treat discrete acoustic codes as textual data and train a masked language model using a cloze-like methodology,ultimately deriving high-quality audio representations. Notably,the MPC learning technique effectively captures collaborative representations among distinct positive samples. Our research outcomes demonstrate that AudioFormer attains significantly improved performance compared to prevailing monomodal audio classification models across multiple datasets,and even outperforms audio-visual multimodal classification models on select datasets. Specifically,our approach achieves remarkable results on datasets including AudioSet (2M,20K),and FSD50K,with performance scores of 53.9,45.1,and 65.6,respectively. We have openly shared both the code and models: https://github.com/LZH-0225/AudioFormer.git., Comment: Need to supplement more detailed experiments

Qianting Yang, Jiao Xie, Yan Cai, Na Wang, Yan Wang, Li Zhang, Youjia Li, Jingjie Yu, Ya Li, Haitao Wang, Kanghuai Zhang Department of Pharmacy, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of ChinaCorrespondence: Kanghuai Zhang, Department of Pharmacy, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157 Xiwu Road, Xi’an, 710004, People’s Republic of China, Tel +86-29-87679282, Email zhangkanghuai12@163.comPurpose: To determine whether combinations of antifungal drugs are effective and safe for patients in intensive-care units.Methods: This study compared the efficacy and safety of caspofungin (CAS), voriconazole (VOR), amphotericin B liposome (L-AmB), CAS+VOR, and CAS+L-AmB as empirical, preemptive, and targeted therapies for invasive fungal infection (IFI).Results: Comparing the CAS, VOR, and CAS+VOR groups revealed that there were no differences in response rates between all therapy types, IFI-associated death within 90 days was less common in the CAS+VOR group (1.8%) than the VOR group (14.3%), and there were more adverse events in the VOR group than in the CAS group (P < 0.05). For empirical or preemptive therapy, the CAS group had a better response rate (80.0%) than the CAS+VOR group (47.1%), and there were more adverse events in the VOR group than in the CAS group (P < 0.05). For targeted therapy, no differences were found for efficacy and safety. There were no differences among the CAS, L-AmB, and CAS+L-AmB groups in efficacy and safety.Conclusion: Patients who received CAS monotherapy as an empirical or preemptive therapy could achieve good outcomes. Patients who received CAS+VOR or CAS+L-AmB achieved almost the same outcomes when compared with those who received CAS, VOR, and L-AmB monotherapy as targeted therapies, but those who received CAS+VOR had a lower IFI mortality rate than did those who received VOR monotherapy.Keywords: caspofungin, voriconazole, amphotericin B liposome

Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable evaluation results. In this paper, we propose a model-agnostic dataset evaluation framework for automatic dataset quality evaluation. We seek the statistical properties of the datasets and address three fundamental dimensions: reliability, difficulty, and validity, following a classical testing theory. Taking the Named Entity Recognition (NER) datasets as a case study, we introduce $9$ statistical metrics for a statistical dataset evaluation framework. Experimental results and human evaluation validate that our evaluation framework effectively assesses various aspects of the dataset quality. Furthermore, we study how the dataset scores on our statistical metrics affect the model performance, and appeal for dataset quality evaluation or targeted dataset improvement before training or testing models.

The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Due to the unstructured environment and high uncertainties, battery disassembly is still primarily done by humans, probably assisted by robots. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel framework of the NeuroSymbolic task and motion planning method to disassemble batteries in an unstructured environment using robots automatically. It enables robots to independently locate and disassemble battery bolts, with or without obstacles. This study not only provides a solution for intelligently disassembling electric vehicle batteries but also verifies its feasibility through a set of test results with the robot accomplishing the disassembly tasks in a complex and dynamic environment., Comment: Accepted to IntelliSys 2022(Intelligent Systems Conference),15 pages with 6 figures

Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matou\v{s}ek's results, we can build a data structure of $O(n)$ space so that each query can be answered in $O(\sqrt{n})$ time. Our techniques lead to improvements for several other classical problems, such as batched range searching, counting/reporting intersecting pairs of unit circles, distance selection, discrete 2-center, etc. For example, given a set of $n$ unit disks and a set of $n$ points in the plane, the batched range searching problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\log n)$ time while our new algorithm runs in $O(n^{4/3})$ time., Comment: Accepted to SWAT 2022. Abstract shortened to fit arXiv requirements

Given a set $P$ of $n$ points that are moving in the plane, we consider the problem of computing a spanning tree for these moving points that does not change its combinatorial structure during the point movement. The objective is to minimize the bottleneck weight of the spanning tree (i.e., the largest Euclidean length of all edges) during the whole movement. The problem was solved in $O(n^2)$ time previously [Akitaya, Biniaz, Bose, De Carufel, Maheshwari, Silveira, and Smid, WADS 2021]. In this paper, we present a new algorithm of $O(n^{4/3} \log^3 n)$ time., Comment: A preliminary version to appear in MFCS 2022

The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Currently, battery disassembly is still primarily done by humans, probably assisted by robots, due to the unstructured environment and high uncertainties. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel neurosymbolic method, which augments the traditional Variational Autoencoder (VAE) model to learn symbolic operators based on raw sensory inputs and their relationships. The symbolic operators include a probabilistic state symbol grounding model and a state transition matrix for predicting states after each execution to enable autonomous task and motion planning. At last, the method's feasibility is verified through test results.

Given a set P of n points that are moving in the plane, we consider the problem of computing a spanning tree for these moving points that does not change its combinatorial structure during the point movement. The objective is to minimize the bottleneck weight of the spanning tree (i.e., the largest Euclidean length of all edges) during the whole movement. The problem was solved in O(n²) time previously [Akitaya, Biniaz, Bose, De Carufel, Maheshwari, Silveira, and Smid, WADS 2021]. In this paper, we present a new algorithm of O(n^{4/3} log³ n) time.

BackgroundAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a recently discovered autoimmune syndrome associated with psychosis, dyskinesia, and seizures. However, the underlying mechanisms of this disease remain unclear, in part because of a lack of suitable animal models.MethodsThis study describes a novel female C57BL/6 mouse model of anti-NMDAR encephalitis that was induced by active immunization against NMDARs using an amino terminal domain (ATD) peptide from the GluN1 subunit (GluN1356-385).ResultsTwelve weeks after immunization, the immunized mice showed significant memory loss. Furthermore, antibodies from the cerebrospinal fluid of immunized mice decreased the surface NMDAR cluster density in hippocampal neurons which was similar to the effect induced by the anti-NMDAR encephalitis patients' antibodies. Immunization also impaired long-term potentiation at Schaffer collateral-CA1 synapses and reduced NMDAR-induced calcium influx.ConclusionWe established a novel anti-NMDAR encephalitis model using active immunization with peptide GluN1356-385 targeting the ATD of GluN1. This novel model may allow further research into the pathogenesis of anti-NMDAR encephalitis and aid in the development of new therapies for this disease.

BackgroundAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a recently discovered autoimmune syndrome associated with psychosis, dyskinesia, and seizures. However, the underlying mechanisms of this disease remain unclear, in part because of a lack of suitable animal models.MethodsThis study describes a novel female C57BL/6 mouse model of anti-NMDAR encephalitis that was induced by active immunization against NMDARs using an amino terminal domain (ATD) peptide from the GluN1 subunit (GluN1356-385).ResultsTwelve weeks after immunization, the immunized mice showed significant memory loss. Furthermore, antibodies from the cerebrospinal fluid of immunized mice decreased the surface NMDAR cluster density in hippocampal neurons which was similar to the effect induced by the anti-NMDAR encephalitis patients' antibodies. Immunization also impaired long-term potentiation at Schaffer collateral-CA1 synapses and reduced NMDAR-induced calcium influx.ConclusionWe established a novel anti-NMDAR encephalitis model using active immunization with peptide GluN1356-385 targeting the ATD of GluN1. This novel model may allow further research into the pathogenesis of anti-NMDAR encephalitis and aid in the development of new therapies for this disease.

Let $L$ be a set of $n$ lines in the plane. The zone $Z(\ell)$ of a line $\ell$ in the arrangement $\mathcal{A}(L)$ of $L$ is the set of faces of $\mathcal{A}(L)$ whose closure intersects $\ell$. It is known that the combinatorial size of $Z(\ell)$ is $O(n)$. Given $L$ and $\ell$, computing $Z(\ell)$ is a fundamental problem. Linear-time algorithms exist for computing $Z(\ell)$ if $\mathcal{A}(L)$ has already been built, but building $\mathcal{A}(L)$ takes $O(n^2)$ time. On the other hand, $O(n\log n)$-time algorithms are also known for computing $Z(\ell)$ without relying on $\mathcal{A}(L)$, but these algorithms are relatively complicated. In this paper, we present a simple algorithm that can compute $Z(\ell)$ in $O(n\log n)$ time. More specifically, once the sorted list of the intersections between $\ell$ and the lines of $L$ is known, the algorithm runs in $O(n)$ time. A big advantage of our algorithm, which mainly involves a Graham's scan style procedure, is its simplicity., Comment: To appear in SOSA 2022

We present new algorithms for computing many faces in arrangements of lines and segments. Given a set $S$ of $n$ lines (resp., segments) and a set $P$ of $m$ points in the plane, the problem is to compute the faces of the arrangements of $S$ that contain at least one point of $P$. For the line case, we give a deterministic algorithm of $O(m^{2/3}n^{2/3}\log^{2/3} (n/\sqrt{m})+(m+n)\log n)$ time. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\log^{2.22}n$ and improves the previously best randomized algorithm [Agarwal, Matou\v{s}ek, and Schwarzkopf, 1998] by a factor of $\log^{1/3}n$ in certain cases (e.g., when $m=\Theta(n)$). For the segment case, we present a deterministic algorithm of $O(n^{2/3}m^{2/3}\log n+\tau(n\alpha^2(n)+n\log m+m)\log n)$ time, where $\tau=\min\{\log m,\log (n/\sqrt{m})\}$ and $\alpha(n)$ is the inverse Ackermann function. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\log^{2.11}n$ and improves the previously best randomized algorithm [Agarwal, Matou\v{s}ek, and Schwarzkopf, 1998] by a factor of $\log n$ in certain cases (e.g., when $m=\Theta(n)$). We also give a randomized algorithm of $O(m^{2/3}K^{1/3}\log n+\tau(n\alpha(n)+n\log m+m)\log n\log K)$ expected time, where $K$ is the number of intersections of all segments of $S$. In addition, we consider the query version of the problem, that is, preprocess $S$ to compute the face of the arrangement of $S$ that contains any query point. We present new results that improve the previous work for both the line and the segment cases., Comment: To be presented at SODA 2022

The electrochemical reduction of nitrogen, as a sustainable alternative to the known Haber-Bosch process, possesses promising application prospects in the development of renewable energy storage systems. However, the yield of NH3 and Faraday efficiency are usually very low owing to the loss of active electrocatalysts and competitive hydrogen evolution reactions. Herein, uniform NiS@MoS2 core-shell microspheres are controllably prepared as a potential catalyst for an ambient electrocatalytic N-2 reduction reaction. The NiS@MoS2 microspheres possess highly active intrinsic, sufficient accessible active sites, high structural porosity, and convenient transport channels, consequently boosting the transmission of electrons and mass. Additionally, the interfacial interaction between NiS and MoS2 facilitates electron transfer, which further improves the catalytic activity by optimizing the free energies of reaction intermediates. As a result, the titled NiS@MoS2 shows excellent electrochemical activity and selectivity, capable of achieving a relatively high NH3 yield of 9.66 mu g h(-1) mg(cat)(-1) at -0.3 V (vs. the reversible hydrogen electrode, RHE) and a high FE of 14.8% at -0.1 V vs. RHE in 0.1 M Na2SO4. The work demonstrated here may open a new avenue for the rational design and synthesis of catalysts for the electrochemical synthesis of ammonia., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [21601120, 21805181]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M611529]; Science and Technology Commission of Shanghai MunicipalityScience & Technology Commission of Shanghai Municipality (STCSM) [17ZR1410500, 19ZR1418100]

The use of multi-modal data such as the combination of whole slide images (WSIs) and gene expression data for survival analysis can lead to more accurate survival predictions. Previous multi-modal survival models are not able to efficiently excavate the intrinsic information within each modality. Moreover, previous methods regard the information from different modalities as similarly important so they cannot flexibly utilize the potential connection between the modalities. To address the above problems, we propose a new asymmetrical multi-modal method, termed as AMMASurv. Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance. Encouraging experimental results demonstrate the superiority of our method over other state-of-the-art methods., Comment: 4 pages. Accepted by IEEE BIBM 2021

Given a set $P$ of $n$ points and a set $S$ of $m$ weighted disks in the plane, the disk coverage problem asks for a subset of disks of minimum total weight that cover all points of $P$. The problem is NP-hard. In this paper, we consider a line-constrained version in which all disks are centered on a line $L$ (while points of $P$ can be anywhere in the plane). We present an $O((m+n)\log(m+n)+\kappa\log m)$ time algorithm for the problem, where $\kappa$ is the number of pairs of disks that intersect. Alternatively, we can also solve the problem in $O(nm\log(m+n))$ time. For the unit-disk case where all disks have the same radius, the running time can be reduced to $O((n+m)\log(m+n))$. In addition, we solve in $O((m+n)\log(m+n))$ time the $L_{\infty}$ and $L_1$ cases of the problem, in which the disks are squares and diamonds, respectively. As a by-product, the 1D version of the problem where all points of $P$ are on $L$ and the disks are line segments on $L$ is also solved in $O((m+n)\log(m+n))$ time. We also show that the problem has an $\Omega((m+n)\log (m+n))$ time lower bound even for the 1D case. We further demonstrate that our techniques can also be used to solve other geometric coverage problems. For example, given in the plane a set $P$ of $n$ points and a set $S$ of $n$ weighted half-planes, we solve in $O(n^4\log n)$ time the problem of finding a subset of half-planes to cover $P$ so that their total weight is minimized. This improves the previous best algorithm of $O(n^5)$ time by almost a linear factor. If all half-planes are lower ones, then our algorithm runs in $O(n^2\log n)$ time, which improves the previous best algorithm of $O(n^4)$ time by almost a quadratic factor., Comment: A preliminary version to appear in WADS 2021

Given a set P of n points in the plane, the unit-disk graph G_{r}(P) with respect to a parameter r is an undirected graph whose vertex set is P such that an edge connects two points p, q \in P if the Euclidean distance between p and q is at most r (the weight of the edge is 1 in the unweighted case and is the distance between p and q in the weighted case). Given a value \lambda>0 and two points s and t of P, we consider the following reverse shortest path problem: computing the smallest r such that the shortest path length between s and t in G_r(P) is at most \lambda. In this paper, we present an algorithm of O(\lfloor \lambda \rfloor \cdot n \log n) time and another algorithm of O(n^{5/4} \log^{7/4} n) time for the unweighted case, as well as an O(n^{5/4} \log^{5/2} n) time algorithm for the weighted case. We also consider the L_1 version of the problem where the distance of two points is measured by the L_1 metric; we solve the problem in O(n \log^3 n) time for both the unweighted and weighted cases., Comment: This version added new results on the weighted case and the L1 case. These new results have been accepted in WALCOM 2022 and have also been presented at FWCG 2021

Given a set of pairwise disjoint polygonal obstacles in the plane, finding an obstacle-avoiding Euclidean shortest path between two points is a classical problem in computational geometry and has been studied extensively. Previously, Hershberger and Suri [SIAM J. Comput. 1999] gave an algorithm of $O(n\log n)$ time and $O(n\log n)$ space, where $n$ is the total number of vertices of all obstacles. Recently, by modifying Hershberger and Suri's algorithm, Wang [SODA 2021] reduced the space to $O(n)$ while the runtime of the algorithm is still $O(n\log n)$. In this paper, we present a new algorithm of $O(n+h\log h)$ time and $O(n)$ space, provided that a triangulation of the free space is given, where $h$ is the number of obstacles. The algorithm, which improves the previous work when $h=o(n)$, is optimal in both time and space as $\Omega(n+h\log h)$ is a lower bound on the runtime. Our algorithm builds a shortest path map for a source point $s$, so that given any query point $t$, the shortest path length from $s$ to $t$ can be computed in $O(\log n)$ time and a shortest $s$-$t$ path can be produced in additional time linear in the number of edges of the path., Comment: To appear in STOC 2021

Given a set $S$ of $m$ point sites in a simple polygon $P$ of $n$ vertices, we consider the problem of computing the geodesic farthest-point Voronoi diagram for $S$ in $P$. It is known that the problem has an $\Omega(n+m\log m)$ time lower bound. Previously, a randomized algorithm was proposed [Barba, SoCG 2019] that can solve the problem in $O(n+m\log m)$ expected time. The previous best deterministic algorithms solve the problem in $O(n\log \log n+ m\log m)$ time [Oh, Barba, and Ahn, SoCG 2016] or in $O(n+m\log m+m\log^2 n)$ time [Oh and Ahn, SoCG 2017]. In this paper, we present a deterministic algorithm of $O(n+m\log m)$ time, which is optimal. This answers an open question posed by Mitchell in the Handbook of Computational Geometry two decades ago., Comment: To appear in SoCG 2021

Electrochemical splitting of water is one of the most reliable and effective ways for the sustainable production of pure hydrogen on a large scale, while the core of this technology lies in the development of highly active non-noble-metal-based electrocatalysts to lower the large dynamic overpotentials of electrode materials. Here, an interface engineering strategy is demonstrated to construct an efficient and stable catalyst based on NiS@MoS2 core-shell hierarchical microspheres for the hydrogen evolution reactions (HER). The ultrathin MoS2 nanosheets in-situ grow on the surface of NiS hierarchical micro-sized spheres constructed by porous nanoplates, endowing the composites with rich interfaces, well-exposed electroactive edges, high structural porosity and fast transport channels. These advantages are favorable for the improvement of catalytic sites and the transport of catalysis-relevant species. More importantly, the intimate contact between MoS2 nanosheets and NiS nanoplates synergistically favors the chemical sorption of hydrogen intermediates, thereby reducing the reaction barrier and accelerating the HER catalytic process. As a result, the optimized NiS@MoS2 catalyst manifests impressive HER activity and durability, with a low overpotential of 208 mV in 0.5 M H2SO4 and 146 mV in 1.0 M KOH at 10 mA cm(-2), respectively. This work not only provides an effective way to construct core-shell hierarchical microspheres but also a multiscale strategy to regulate the electronic structure of heterostructured materials for energy-related applications. (C) 2020 Elsevier B.V. All rights reserved., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [21601120, 21805181]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M611529]; Science and Technology Commission of Shanghai MunicipalityScience & Technology Commission of Shanghai Municipality (STCSM) [17ZR1410500, 19ZR1418100]

Aerated topsoils are important sinks for atmospheric methane (CH4) via oxidation by CH4‐oxidizing bacteria (MOB). However, intensified management of grasslands and forests may reduce the CH4 sink capacity of soils. We investigated the influence of grassland land‐use intensity (150 sites) and forest management type (149 sites) on potential atmospheric CH4 oxidation rates (PMORs) and the abundance and diversity of MOB (with qPCR) in topsoils of three temperate regions in Germany. PMORs measurements in microcosms under defined conditions yielded approximately twice as much CH4 oxidation in forest than in grassland soils. High land‐use intensity of grasslands had a negative effect on PMORs (−40%) in almost all regions and fertilization was the predominant factor of grassland land‐use intensity leading to PMOR reduction by 20%. In contrast, forest management did not affect PMORs in forest soils. Upland soil cluster (USC)‐α was the dominant group of MOBs in the forests. In contrast, USC‐γ was absent in more than half of the forest soils but present in almost all grassland soils. USC‐α abundance had a direct positive effect on PMOR in forest, while in grasslands USC‐α and USC‐γ abundance affected PMOR positively with a more pronounced contribution of USC‐γ than USC‐α. Soil bulk density negatively influenced PMOR in both forests and grasslands. We further found that the response of the PMORs to pH, soil texture, soil water holding capacity and organic carbon and nitrogen content differ between temperate forest and grassland soils. pH had no direct effects on PMOR, but indirect ones via the MOB abundances, showing a negative effect on USC‐α, and a positive on USC‐γ abundance. We conclude that reduction in grassland land‐use intensity and afforestation has the potential to increase the CH4 sink function of soils and that different parameters determine the microbial methane sink in forest and grassland soils., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659, ESF, Ministry of Education, Science and Culture of Mecklenburg‐Western Pomerania, Peer Reviewed

The prognostic power of circulating cardiac biomarkers, their utility, and pattern of release in coronavirus disease 2019 (COVID-19) patients have not been clearly defined. In this multicentered retrospective study, we enrolled 3219 patients with diagnosed COVID-19 admitted to 9 hospitals from December 31, 2019 to March 4, 2020, to estimate the associations and prognostic power of circulating cardiac injury markers with the poor outcomes of COVID-19. In the mixed-effects Cox model, after adjusting for age, sex, and comorbidities, the adjusted hazard ratio of 28-day mortality for hs-cTnI (high-sensitivity cardiac troponin I) was 7.12 ([95% CI, 4.60-11.03] P<0.001), (NT-pro)BNP (N-terminal pro-B-type natriuretic peptide or brain natriuretic peptide) was 5.11 ([95% CI, 3.50-7.47] P<0.001), CK (creatine phosphokinase)-MB was 4.86 ([95% CI, 3.33-7.09] P<0.001), MYO (myoglobin) was 4.50 ([95% CI, 3.18-6.36] P<0.001), and CK was 3.56 ([95% CI, 2.53-5.02] P<0.001). The cutoffs of those cardiac biomarkers for effective prognosis of 28-day mortality of COVID-19 were found to be much lower than for regular heart disease at about 19%-50% of the currently recommended thresholds. Patients with elevated cardiac injury markers above the newly established cutoffs were associated with significantly increased risk of COVID-19 death. In conclusion, cardiac biomarker elevations are significantly associated with 28-day death in patients with COVID-19. The prognostic cutoff values of these biomarkers might be much lower than the current reference standards. These findings can assist in better management of COVID-19 patients to improve outcomes. Importantly, the newly established cutoff levels of COVID-19-associated cardiac biomarkers may serve as useful criteria for the future prospective studies and clinical trials.

© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Carbon is a simple, stable and popular element with many allotropes. The carbon family members include carbon dots, carbon nanotubes, carbon fibers, graphene, graphite, graphdiyne and hard carbon, etc. They can be divided into different dimensions, and their structures can be open and porous. Moreover, it is very interesting to dope them with other elements (metal or non-metal) or hybridize them with other materials to form composites. The elemental and structural characteristics offer us to explore their applications in energy, environment, bioscience, medicine, electronics and others. Among them, energy storage and conversion are extremely attractive, as advances in this area may improve our life quality and environment. Some energy devices will be included herein, such as lithium-ion batteries, lithium sulfur batteries, sodium-ion batteries, potassium-ion batteries, dual ion batteries, electrochemical capacitors, and others. Additionally, carbon-based electrocatalysts are also studied in hydrogen evolution reaction and carbon dioxide reduction reaction. However, there are still many challenges in the design and preparation of electrode and electrocatalytic materials. The research related to carbon materials for energy storage and conversion is extremely active, and this has motivated us to contribute with a roadmap on ‘Carbon Materials in Energy Storage and Conversion’.

© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Carbon is a simple, stable and popular element with many allotropes. The carbon family members include carbon dots, carbon nanotubes, carbon fibers, graphene, graphite, graphdiyne and hard carbon, etc. They can be divided into different dimensions, and their structures can be open and porous. Moreover, it is very interesting to dope them with other elements (metal or non-metal) or hybridize them with other materials to form composites. The elemental and structural characteristics offer us to explore their applications in energy, environment, bioscience, medicine, electronics and others. Among them, energy storage and conversion are extremely attractive, as advances in this area may improve our life quality and environment. Some energy devices will be included herein, such as lithium-ion batteries, lithium sulfur batteries, sodium-ion batteries, potassium-ion batteries, dual ion batteries, electrochemical capacitors, and others. Additionally, carbon-based electrocatalysts are also studied in hydrogen evolution reaction and carbon dioxide reduction reaction. However, there are still many challenges in the design and preparation of electrode and electrocatalytic materials. The research related to carbon materials for energy storage and conversion is extremely active, and this has motivated us to contribute with a roadmap on ‘Carbon Materials in Energy Storage and Conversion’.