Miracle-dz/UniFAR-FTUs huggingface dataset:
THE DIET OF THE COMMON MEDUSA SPECIES IN THE EAST CHINA SEA REVEALED BY FATTY ACID AND STABLE CARBON AND NITROGEN ISOTOPIC ANALYSIS

Fatty acid compositions and stable isotope ratios of carbon and nitrogen were used to identify the diet of six common medusa species in the East China Sea,Ocyropsis crystalline,Bero cucumis,Liriope tetraphylla,Diphyidae spp. ,Aequorea coerulescens,and Nemopilema nomurai.Analysis of the fatty acid composition showed that the total fatty acids of three medusae were low,which reflecting their lower lipid nutritional values.O.crystalline,B.cucumis and L.tetraphylla showed similar fatty acid compositions.Firstly,they all had a higher content of 16:1ω7 and 18:4ω3,indicating feeding on phtytoplankton(mostly diatoms and dinoflagellates) more than the other medusae.Secondly,the higher contents of 20:1+22:1 reflected herbivorous copepods are parts of their food items.In addition,they were characterized by high contents of Σω3,accumulated via the grazing food chain.By contrast,A.coerulescens was characterized by an increase in Σω6 of macroalgal origin,which was transferred via the detritus food chain.Otherwise,it also fed on herbivorous copepods in view of the fatty acid 20:1+22:1.The higher Σω6 contents also indicated that Diphyidae spp.accumulated via the detritus food chain,and it fed little on herbivorous copepods.The δ13C of O.crystalline,B.cucumis,L.tetraphylla and N.nomurai were similar to C4 plants,indicating significant contribution of C4 plants to the nutritions of four medusae.The δ15N of the six medusae indicated their higher trophic levels in plankton food web.Although N.nomurai is big,it located at a similar trophic level as O.crystalline,B.cucumis and L.tetraphylla,because of its smaller terminal pore.In addition,the marker of Bacteria FA was evident in N.nomurai.
Lifted Heuristics for Timeline-Based Planning

This paper discusses the issue of efﬁcient resolution of timeline-based planning problems. In particular, taking inspiration from the more classical heuristics for the resolution of STRIPS-like problems, it proposes a new heuristic strategy which, while maintaining the variables lifted, allows more accurate decisions. The concepts presented in this work pave the way for a new type of heuristics which, at present, allow this kind of solvers a signiﬁcant performance improvement
DYNAMICS IN THE SUCCESSION OF VEGETATION COMMUNITIES IN INNER-RIVER WETLAND IN ZHENJIANG

By using the spatial series to replace temporal courses,the authors selected four typical sampling sites representing different successive stages in inner-river wetland in Zhenjiang City,and then investigated the laws of vegetation community,e.g.,species composition,species diversity and life-form in order to establish restoration approaches for degraded vegetation communities.The species diversity,species composition and life-form along different successive stages differed from each other significantly.In general,there was an increasing trend in species richness and species diversity along different successive stages,with the highest richness and diversity observed in the mid-last successive stages;the evenness degree increased continuously,while ecological dominance declined progressively along with succession.For lifeform composition,along with succession process,the importance value of perennial herbaceous plants declined firstly and then increased,while the species number of perennial herbaceous plants increased gradually and became relatively steady in later successive stages,and perennial herbaceous plants were dominant in all successive stages.As to annual-biennial herbaceous plants,the importance value and species number increased firstly and then declined.The total number of family and genera increased gradually as succession proceeded,and Gramineae were dominant species at all time.
Point-Of-Interest Semantic Tag Completion in a Global Crowdsourced Search-and-Discovery Database

Applications that process Point-of-Interest data are omni-present nowadays. They range from digital maps to recommender systems for places to visit, and personal assistants. The success of such applications critically depends on the quality of the ingested data. However, corresponding databases, especially when they are crowdsourced, are often incomplete. Existing work on automatic data completion approaches has only partially considered the task for Points-of-Interest (POI). Such entities have a number of distinctive properties - notably multiscript names, geo-spatial identity, and temporally deﬁned context -, which make the task more challenging. Here we present an approach to automatically complete POI semantic tags in a crowdsourced database. We perform experiments on multi-lingual data from Foursquare, a global location-based social network, and observe that (i) POI names are strong predictors of POI semantic tags: a character-based LSTM model trained only on POI names gives 72.5% worst-case micro-precision and 50.38% micro-F1 scores, (ii) appropriate use of spatio-temporal data leads to consistent improvements (iii) using a structured representation of time gives higher precision and requires less computation time than string-based LSTM variants, however, the higher precision is achieved at a cost of lower recall and micro-F1, (iv) an LSTM model trained on semi-structured strings representing time, is competitive to fully structured inputs in terms of recall.
Healing in Horses

In what case do you like reading so much? What about the type of the healing for horses book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.
A Two-Stage Multi-Objective Deep Reinforcement Learning Framework

In multi-objective decision making problems, multi-objective reinforcement learning (MORL) algorithms aim to approximate the Pareto frontier uniformly. A naive approach is to learn multiple policies by repeatedly running a single-objective reinforcement learning (RL) algorithm on scalarized rewards. The scalarization methods denote the preferences of objectives, which are different in each run. However, in this way, the model representation and computation are redundant. Furthermore, uniform preferences can not guarantee a uniformly approximated Pareto frontier. To address these problems and leverage the expressive power of deep neural networks, we propose a two-stage MORL framework integrating a multi-policy deep RL algorithm and an evolution strategy algorithm. Firstly, a multi-policy soft actor-critic algorithm is proposed to collaboratively learn multiple policies which are assigned with different scalarization weights. The lower layers of all policy networks are shared. The ﬁrst-stage learning can be regarded as representation learning. Secondly, the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is applied to ﬁne-tune policy-independent parameters to approach a dense and uniform estimation of the Pareto frontier. Experimental results on two benchmarks (Deep Sea Treasure and Adaptive Streaming) show the superiority of the proposed method.
From Public Announcements to Asynchronous Announcements

We present a multi-agent logic of belief and announcements wherein the sending of announcements and the reception of announcements by agents are separated, thus straying from the paradigm of Public Announcement Logic (PAL). Both PAL and Asynchronous Announcement Logic (recently proposed in the literature) are special cases in our framework. We provide a history-based semantics for our ‘Partially Synchronous Announcement Logic’, proposing three different interpretations of the notion of asynchronic-ity. We then show that the logic of our three proposals is the same (‘PSAL’) and prove soundness and completeness for a Hilbert-style axiomatisation. Finally, we propose a notion of common belief for this framework, of which we give some validities.
Point Cloud Segmentation with Deep Reinforcement Learning

The segmentation of point clouds is conducted with the help of deep reinforcement learning (DRL) in this contribution. We want to create interactive virtual reality (VR) environments from point cloud scans as fast as possible. These VR environments are used for secure and immersive trainings of serious real life applications such as the extinguishing of a ﬁre. It is necessary to segment the point cloud scans to create interactions in the VR. Existing geometric and semantic point cloud segmentation approaches are not powerful enough to automatically segment point cloud scenes that consist of diverse unknown objects. Hence, we tackle this problem by considering point cloud segmentation as markov decision process and applying DRL. More speciﬁcally, a deep neural network (DNN) sees a point cloud as state, estimates the parameters of a region growing algorithm and earns a reward value. The point cloud scenes originate from virtual mesh scenes that were transformed to point clouds. Thus, a point to segment relationship exists that is used in the reward function. Moreover, the reward function is developed for our case where the true segments do not correspond to the assigned segments. This case results from, but is not limited to, the usage of the region growing algorithm. Several experiments with different point cloud DNN architectures such as PointNet [13] are conducted. We show promising results for the future directions of the segmentation of point clouds with DRL.
VT-LINKER: Visual-Textual-Knowledge Entity Linker

“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efﬁciently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either ﬁnding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we precisely deﬁne the VTKEL task and present two datasets composed of 1k and 30k pictures, annotated with visual and textual entities and linked to the YAGO ontology. Successively, we develop the ﬁrst unsupervised algorithm for the solution of VTKEL task. The evaluation of the algorithm shows promising results on both 1k and 30k VTKEL datasets.
ST-MFM: A Spatiotemporal Multi-Modal Fusion Model for Urban Anomalies Prediction

Urban anomaly prediction is of great importance for urban management and public safety. Accurate anomaly prediction can avoid much unnecessary loss. Urban anomalies are usually caused by many complex factors, such as festivals, demonstrations and market promotions. It is not possible to predict anomalies from the perspective of reason, thus, most of the previous work analyzes the impacts of anomalies from multiple crowd ﬂow datasets and observes the shift to ordinary distribution when they occur. Most existing models use observation-based methods to extract relevant spatiotemporal features, which are difﬁcult to fully extract hidden relationships and eventually lead to low accuracy and low recall. In this paper, we propose an end-to-end deep learning based approach, called spatiotemporal multi-modal fusion model to collect the impacts of urban anomalies on multiple crowd ﬂow datasets and predict anomalies in each region of the city for next time interval in turn. More speciﬁcally, we model the city into a graph and regard each region as a node. We use graph convolution network to obtain its spatial features and use gate recurrent units to obtain its temporal features. The features of those multiple modalities are further aggregated with points of interest in a two-stage-fusion method for assigning different weights to different functional regions. We evaluate our method using ﬁve datasets associated with New York City: 311 complaints, taxicab data, bike rental data, points of interest and road network dataset. Results show the advantages nearly 10% beyond the-state-of-the-art urban anomalies prediction methods.
Polygenic Adaptation in a Population of Finite Size

Polygenic adaptation in response to selection on quantitative traits has become an important topic in evolutionary biology. Here we review the recent literature on models of polygenic adaptation. In particular, we focus on a model that includes mutation and both directional and stabilizing selection on a highly polygenic trait in a population of finite size (thus experiencing random genetic drift). Assuming that a sudden environmental shift of the fitness optimum occurs while the population is in a stochastic equilibrium, we analyze the adaptation of the trait to the new optimum. When the shift is not too large relative to the equilibrium genetic variance and this variance is determined by loci with mostly small effects, the approach of the mean phenotype to the optimum can be approximated by a rapid exponential process (whose rate is proportional to the genetic variance). During this rapid phase the underlying changes to allele frequencies, however, may depend strongly on genetic drift. While trait-increasing alleles with intermediate equilibrium frequencies are dominated by selection and contribute positively to changes of the trait mean (i.e., are aligned with the direction of the optimum shift), alleles with low or high equilibrium frequencies show more of a random dynamics, which is expected when drift is dominating. A strong effect of drift is also predicted for population size bottlenecks. Our simulations show that the presence of a bottleneck results in a larger deviation of the population mean of the trait from the fitness optimum, which suggests that more loci experience the influence of drift.
Proposing a trend-based time-varying approach to assess climate- and human-induced impacts on streamflow

ABSTRACT The conventional abrupt change-based assessments of climate- and human-induced impacts on streamflow require the existence of change point(s) and stationarity assumption. However, hydrological conditions may not change abruptly at a certain time, but rather evolve gradually over a period. We propose a trend-based time-varying approach that does not require these prerequisites to assess the climate- and human-induced impacts on hydrological conditions in the Pearl River Basin (PRB), China, which can be applied in other basins. The trend-based time-varying approach detects human activities exert a significant seasonal regulation on streamflow (i.e. 113% of the decreases in the wet season and 93% of the increases in the dry season) and 101% of the reductions in flood peaks in the East River Basin, the sub-basin with the highest ratio of total reservoir storage capacity to river discharge in the PRB. Climate change contributes to 77% of the increases in flood peaks in the West River Basin, a large sub-basin with lower flood control levels.
Fungal diversity in deep-sea sediments from the Magellan seamounts as revealed by a metabarcoding approach targeting the ITS2 regions

ABSTRACT Recent reports have revealed diverse and abundant fungal communities in the deep-sea biosphere, while their composition, distribution, and variations in seamount zones are poorly understood. Using a metabarcoding approach targeting the ITS2 regions, we present the structure of the fungal community in 18 sediment samples from the Magellan seamount area of the northwest Pacific. A total of 1,979 fungal OTUs was obtained, which were taxonomically assigned to seven phyla, 17 classes, 43 orders, 7 families, and 98 genera. The majority of these OTUs were affiliated to Basidiomycota (873 OTUs, 44.11% of total OTUs) and Ascomycota (486 OTUs, 24.56% of total OTUs), followed by other five minor phyla (Mortierellomycota, Chytridiomycota, Mucoromycota, Glomeromycota, and Monoblepharidomycota). Sordriomycetes is the most abundant class, followed by Eurotiomycetes, and Dothideomycetes. Five genera were common in most of the samples, including worldwide reported genera Aspergillus, Cladosporium, Fusarium, Chaetomium, and Penicillium. The environmental data we collected (sampling depth, sampling location latitude and longitude, organic carbon content, and organic nitrogen content in the sediment) had no significant influence on the composition and distribution of fungal communities. Our findings provide valuable information for understanding the distribution and potential ecological functions of fungi in the deep-sea sediments of the Magellan seamounts.
Association of the MC4R gene expression with intramuscular fat content in Hu sheep muscles

In the study,five male Hu sheep of newborn,1-6 month age and 12 month age were selected,and detected were the melanocortin 4 receptor gene(MC4R)mRNA expression by real-time PCR in longissimus dorsi muscle,psoas major muscle,biceps femoris muscle and biceps brachii muscle,and analyzed was the relationship between the developmental changes of the MC4R mRNA expression and intramuscular fat(IMF)content in muscles.The results showed that the MC4R gene expression patterns of Hu Sheep different muscle parts increased and then decreased.Among them,MC4R gene of longissimus dorsi muscle,biceps femoris muscle and biceps brachii muscle had the highest level at the 3rd month,with a significant difference compared with other months,while psoas major muscle MC4R gene expression had the highest level at the second month.There were significant differences of MC4R mRNA expression level of 5-month-old among different muscle but no significant laws.MC4R mRNA expression levels in longissimus dorsi muscle,psoas major muscle and biceps femoris muscle of Hu sheep was negatively correlated to intramuscular fat content.
Water balance components estimation under scenarios of land cover change in the Vea catchment, West Africa

ABSTRACT The need for a detailed investigation of the Vea catchment water balance components cannot be overemphasized due to its accelerated land-cover dynamics and the associated impacts on the hydrological processes. This study assessed the possible consequences of land-use change scenarios (i.e. business as usual, BAU, and afforestation for the year 2025) compared to the 2016 baseline on the Vea catchment’s water balance components using the Soil and Water Assessment Tool (SWAT) model. The data used include daily climate and discharge, soil and land use/land cover maps. The results indicate that the mean annual water yield may increase by 9.1% under the BAU scenario but decrease by 2.7% under the afforestation scenario; actual evapotranspiration would decrease under BAU but increase under afforestation; and groundwater recharge may increase under both scenarios but would be more pronounced under the afforestation scenario. These outcomes highlight the significance of land-cover dynamics in water resource management and planning at the catchment.
Whiteness of a Darker Shade: Reclassifying Italian-Americans in the Trials of Maria Barbella

It has been established that racial categories are constituted by rhetoric as much as reality and that whiteness occupies the position of greatest power in the resulting hierarchy. Prior research has demonstrated the symbolic constructions that defend the boundaries of whiteness. Those boundaries, however, have historically been unclear and fluid in their application to immigrants. This essay examines that fluidity during the two trials of an Italian immigrant for murder. An immigrant community successfully shifted the borders of white America to encompass Mediterranean immigrants by turning rhetorical strategies of whiteness against themselves through a complex relationship of multiple narratives arising from at least three different communities. It concludes by discussing the opportunities, limitations, and risks for liminal communities seeking to alter their relationship to whiteness.
Sequential hypothesis tests under random horizon

Abstract We consider a problem of sequential testing a simple hypothesis against a simple alternative, based on observations of a discrete-time stochastic process in the presence of a random horizon H. At any time n of the experiment, the statistician is only informed whether H > n or not. In this latter case, the experiment should be terminated and the final decision on the acceptance or rejection of the hypothesis should be taken on the basis of the available observations ( ). H is assumed to be independent of the observations, and its distribution is known to the statistician. Under the random horizon, we consider a variant of the modified Kiefer-Weiss problem: given restrictions on the probabilities of errors, minimize the average sample size calculated under the assumption that the observations follow a fixed distribution, not necessarily one of those hypothesized. Under suitable conditions on the process and/or the horizon, we characterize the structure of all optimal sequential tests in this problem. Then, we apply these results to characterize optimal tests in the case of independent observations. On the basis of the general theory, more specific results are obtained for independent and identically distributed (i.i.d.) observations with a geometrically distributed horizon. In a simple sampling model, we solve the Kiefer-Weiss problem under the random horizon model. We also discuss the questions of Wald-Wolfowitz optimality in the presence of the random horizon. In particular, we show that the stopping rules of the optimal tests, minimizing the average sample size under one of the hypotheses, are randomized versions of those of Wald’s sequential probability ratio tests.
Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Latent Factor Analysis

1 With the rapid development of services computing in the past decade, automatic selection of QoS-aware Web service out from numerous candidates with similar functions is becoming a hot yet thorny issue. Conducting warming up tests on numerous candidate services for quality evaluation is extremely time-consuming and expensive, making it vital to generate highly accurate predictions for missing QoS data based on known ones. Since QoS data are time-dependent, it is vital to consider the temporal dynamic patterns hidden in historical ones when building a QoS-estimator. For addressing this issue, this study invents a Kalman filter-incorporated Latent Factor Analysis (KLFA)-based QoS-estimator, which precisely models the temporal patterns hidden in dynamic QoS data. Experimental results based on large-scale and real-world Web service QoS data demonstrate that compared with state-of-the-art temporal-aware QoS-estimators, a KLFA-based one achieves significantly higher prediction accuracy for missing QoS data.