Miracle-dz/UniFAR-FTUs huggingface dataset:
Hubble: An Industrial System for Audience Expansion in Mobile Marketing

Recently, in order to take a preemptive opportunity in the mobile economy, the Internet companies conduct thousands of marketing campaigns every day, to promote their mobile products and services. In the mobile marketing scenario, one of the fundamental issues is the audience expansion task for marketing campaigns. Given a set of seed users, audience expansion aims to seek more users (audiences), who are similar to the seeds and will finish the business goal of the targeted campaign (ie convert). However, the problem is challenging in three aspects. First, a company will run hundreds of campaigns to serve massive users every day. The requirements of scalability and timeliness make training model for each campaign extremely resource-consuming thus impractical. Therefore, we proposed to solve the problem in a two-stage manner, in which the offline stage employs heavyweight user representation learning and the online stage performs embedding-based lightweight audience expansion. Second, conventional two-stage audience expansion systems neglect the high-order user-campaign interactions and usually generate entangled user embeddings, thus fail to achieve high-quality user representation. Third, the seeds, which are usually provided by experts or collected from users' feedbacks, could be noisy and cannot cover the entire actual audiences, thus introduce coverage bias. Unfortunately, to our best knowledge, none of the related literatures tackle this crucial issue of audience expansion. Addressing the above challenges, in this paper, we present the Hubble System, an industrial solution for audience expansion in mobile marketing scenario. Hubble system follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, we propose a novel adaptive and disentangled graph neural network (called AD-GNN), to adaptively explore the user-campaign graph and generate comprehensive user embedding in a disentangled manner. In the online stage, tackling the coverage bias issue, we develop a novel audience expansion model with knowledge distillation mechanism (called KD-AE), to absorb knowledge from the offline AD-GNN and alleviate the coverage bias.Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed Hubble system, compared with other state-of-the-art methods.
A Semi-Supervised Learning Framework for TRIZ-Based Chinese Patent Classification

Automatic patent classification based on the TRIZ inventive principles is essential for patent management and industrial analysis. However, acquiring labels for deep learning methods is extraordinarily difficult and costly. This paper proposes a new two-stage semi-supervised learning framework called TRIZ-ESSL, which stands for Enhanced Semi-Supervised Learning for TRIZ. TRIZ-ESSL makes full use of both labeled and unlabeled data to improve the prediction performance. TRIZ-ESSL takes the advantages of semi-supervised sequence learning and mixed objective function, a combination of cross-entropy, entropy minimization, adversarial and virtual adversarial loss functions. Firstly, TRIZ-ESSL uses unlabeled data to train a recurrent language model. Secondly, TRIZ-ESSL initializes the weights of the LSTM-based model with the pre-trained recurrent language model and then trains the text classification model using mixed objective function on both labeled and unlabeled sets. On 3 TRIZ-based classification tasks, TRIZ-ESSL outperforms the current popular semi-supervised training methods and Bert in terms of accuracy score.
Influence of Bupleurum Liver-Coursing Powder on Expression of Bax and Bcl-2 Proteins in Rats with Post-stroke Depression

Objective:To observe the post-stroke depression(PSD) model of rat and hippocampal neuronal apoptosis related gene expression changes,and the intervention of Bupleurum Liver-Coursing Powder on for PSD and the molecular mechanism.Method: One hundred male SD rats were randomly divided into the following 3 groups: Bupleurum Liver-Coursing Powder group,PSD model control group and normal control group.Except the normal control group,at first the other two groups were set up middle cerebral artery occlusion(MCAO) rat model,eneurological recovery of stroke rat was assessed by Longa grades standard test.A week after surgery,we re-establish PSD composite rat model,while Bupleurum Liver-Coursing Powder group(7.875 g.kg-1) was fed 21 days with the drugs,PSD model control group was fed distilled water.Immunohistochemical staining detect Bcl-2 and Bax protein expression in hippoca mpus.Result:Compared with the normal control group,Bax protein expression in PSD model group was increased,but Bcl-2 protein decreased(P 0.01);Compared with the PSD model group,Bupleurum Liver-Coursing powder group was significantly reduced in Bax protein expression(P 0.01) and increased in Bcl-2 protein expression(P 0.01). Conclusion:Bupleurum Liver-Coursing Powder may play a role in the treatment of PSD through inhibiting the apoptosis by means of intervention with reduced Bax protein expression and increased the expression of Bcl-2 gene.
Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach

Our model is based on the graph convolutional network (GCN) with node feature (coordination and demand) and edge feature (the real distance between nodes) as input and embedded. Separate decoders are proposed to decode the representations of these two features. The output of one decoder is the supervision of the other decoder. We propose a strategy that combines the reinforcement learning manner with the supervised learning manner to train the model. Through comprehensive experiments on real-world data, we show that 1) the edge feature is important to be explicitly considered in the model; 2) the joint learning strategy can accelerate the convergence of the training and improve the solution quality; 3) our model significantly outperforms several well-known algorithms in the literature, especially when the problem size is large; 3) our method is generalized beyond the size of problem instances they were trained on.
A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce

Shop search has become an increasingly important service provided by Taobao, the China's largest e-commerce platform. By using shop search, a user can easily identify the desired shop that provides a full-scale of relevant items matching his information need. With the tremendous growth of users and shops, shop search faces several unique challenging problems: 1) many shop names do not fully express what they sell, i.e., the semantic gap between user query and shop name; 2) due to the lack of user interactions, it is difficult to deliver a good search result for the long-tail queries and retrieve long-tail shops that are highly relevant to a query. To address these two key challenges, we resort to graph neural networks (GNNs) which have various successful applications in arbitrarily structured graph data. Specifically, we propose a dual heterogeneous graph attention network (DHGAT) integrated with the two-tower architecture, using the user interaction data from both shop search and product search. At first, we build a heterogeneous graph in the context of shop search, by exploiting both the first-order and second-order proximity from user search behaviors, user click-through behaviors and user purchase records. Then, DHGAT is devised to attentively adopt heterogeneous and homogeneous neighbors of query and shop to enhance representations of themselves, which can help relieve the long-tail phenomenon. Besides, DHGAT enriches semantics of query text and shop name by compositing the titles of the relevant items to alleviate the semantic gap. Moreover, to enhance the graph representation learning, we augment DHGAT with a regularized neighbor proximity loss (NPL) to explicitly learn the graph topological structure and train whole framework in an end-to-end fashion. Compelling results from both offline evaluation and online A/B tests demonstrate the superiority of DHGAT over state-of-the-art methods, especially for long-tail queries and shops.
Multidimensional single-cell benchmarking of inducible promoters for precise dynamic control in budding yeast

For quantitative systems biology, simultaneous readout of multiple cellular processes as well as precise, independent control over different genes’ activities are essential. In contrast to readout systems such as fluorescent proteins, control systems such as inducible transcription-factor-promoter systems have only been characterized in an ad hoc fashion, impeding precise system-level manipulations of biological systems and reliable modeling. We designed and performed systematic benchmarks involving easy-to-communicate units to characterize and compare inducible transcriptional systems. We built a comprehensive single-copy library of inducible systems controlling standardized fluorescent protein expression in budding yeast, including GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z3EV system, the blue-light optogenetic systems El222-LIP, El222-GLIP and the red-light inducible PhyB-PIF3 system. To analyze these systems’ dynamic properties, we performed high-throughput time-lapse microscopy. The analysis of >100 000 cell images was made possible by the recently developed convolutional neural network YeaZ. We report key kinetic parameters, scaling of noise levels, impacts on growth, and, crucially, the fundamental leakiness of each system. Our multidimensional benchmarking additionally uncovers unexpected disadvantages of widely used tools, e.g., nonmonotonic activity of the MET3 and GALL promoters, slow off kinetics of the doxycycline and estradiol-inducible systems tetOpr and Z3EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two new tools for controlling gene expression: strongLOV, a more light-sensitive El222 mutant, and ARG3pr that functions as an OR gate induced by the lack of arginine or presence of methionine. To demonstrate the ability to finely control genetic circuits, we experimentally tuned the time between cell cycle Start and mitotic entry in budding yeast, artificially simulating near-wild-type timing. The characterizations presented here define the compromises that need to be made for quantitative experiments in systems and synthetic biology. To calibrate perturbations across laboratories and to allow new inducible systems to be benchmarked, we deposited single-copy reporter yeast strains, plasmids, and computer analysis code in public repositories. Furthermore, this resource can be accessed and expanded through the website https://promoter-benchmark.epfl. ch/.
Learning with Limited Labels via Momentum Damped & Differentially Weighted Optimization

As deep learning-based models are deployed more widely in search & recommender systems, system designers often face the issue of gathering large amounts of well-annotated data to train such neural models. While most user-centric systems rely on interaction signals as implicit feedback to train models, such signals are often weak proxies of user satisfaction, as compared to (say) explicit judgments from users, which are prohibitively expensive to collect. In this paper, we consider the task of learning from limited labeled data, wherein we aim at jointly leveraging strong supervision data (e.g. explicit judgments) along with weak supervision data (e.g. implicit feedback or labels from the related task) to train neural models. We present data mixing strategies based on submodular subset selection, and additionally, propose adaptive optimization techniques to enable the model to differentiate between a strong label data point and a weak supervision data point. Finally, we present two different case-studies (i) user satisfaction prediction with music recommendation and (ii) question-based video comprehension and demonstrate that the proposed adaptive learning strategies are better at learning from limited labels. Our techniques and findings provide practitioners with ways of leveraging external labeled data
Distribution and sources of particulate organic carbon in the Pearl River Estuary in summer 2010

Particulate organic carbon(POC) is one of the main forms of carbon and plays an important role in aquatic ecosystems and global carbon cycling.Marine and terrestrial ecosystems are the two important carbon pools for the estuaries on earth,so the investigation on the POC in estuaries is dramatically practical for the research of global carbon cycling.Generally,the POC in estuaries can be classified into two categories by its source(terrestrial input and marine organism production). Stable carbon isotope(δ13C) and C/N are the two recommendatory methods for measuring and analyzing the source of organic carbon,and the precision of stable carbon isotope method(δ13C) is better than that of C/N method because of the stability of δ13C. The present study aimed to investigate the distribution of POC and its sources in the Pearl River Estuary in summer through the POC measurements of 20 sites in August,2010.The results showed that the concentrations of POC ranged from 98.5 to 1929.8 μg/L(the average of 541.9 μg/L),and the concentrations decreased with the salinity gradient in the northern and middle part of the Pearl River Estuary and then increased from the middle part to the southern part of the Pearl River Estuary,and the concentrations of POC in the bottom waters layers were higher than those in the surface water layers.The total suspended matter(TSM),chlorophyll a and hydrodynamics were the major factors that influenced the POC spatial distribution,and the δ13C values of TSM increased with the salinity gradient and varied from-27.05‰ to-21.03‰(the average of-24.57‰),and the values in the bottom water layers were higher than those in the surface water layers.Our results showed that the terrestrial input and marine phytoplankton production contributed to the POC together in the Pearl River Estuary.According to the analysis of binary mixture model,most POC in the northern and middle part of the Pearl River Estuary was subject to the terrestrial organic carbon,which accounted for 50% to 88%(the average of 64%) of the total POC.In contrast,the marine organic carbon was the main contributor to the POC in the southern part of the Pearl River estuary,and it accounted for 50% to 89%(the average of 68%) of the total POC.Compared with two decades ago,the distribution and source of POC varied significantly,which is mainly due to the decrement of sediment discharge and increment of nutrient inputs into the Pearl River estuary.We predict that the sediment discharge rate of the Pearl River will decrease gradually with the construction of more large reservoirs,and then the contribution of terrestrial organic carbon to the POC in the Pearl River estuary will be decreasingly.
Statelets: high dimensional predominant shapes in dynamic functional network connectivity

Dynamic functional network connectivity (dFNC) analysis has been attracting interest over the past years by elucidating crucial details of brain activation patterns in severe neurological or psychiatric disorders. Consequently, a considerable amount of work has been conducted to analyze dFNC and leverage it for an improved understanding of neuro-dynamics and cognition. However, state-of-art methods mostly focus on fixed patterns of connectivity that reoccur. Such approaches do not capture the more transient feature space of the brain’s neural system and its dynamic properties. Likewise, dynamical system approaches model the system continuously and do not well capture unmodelled heterogeneity across subjects and time. Here, we seek an improved understanding of these connectivity states and the mechanism through which their dynamics vary across individuals. Relying on the concept of shapelets to model the temporal dynamics of functional associations between intrinsic brain networks, we develop the ‘statelet’ - a high dimensional state-shape representation of dynamics. We handle scale differences using the earth mover distance as our similarity metric and utilizing kernel density estimation to build a probability density profile for each shapelet. Unlike clustering the time series, we approximate short, variable length connectivity patterns using the probability density profile of the shapelets from lower dimensions. We apply the proposed approach to study patients with schizophrenia (SZ) exhibit and identify reduced modularity increased statelet recurrence in their brain network organization relative to healthy controls (HC). An analysis of the connections’ consistency across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.
To Tune or Not to Tune?: In Search of Optimal Configurations for Data Analytics

This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data analytics frameworks. These issues include: 1) the assumption of a static workload or environment, ignoring the dynamic characteristics of the analytics environment (e.g., increase in input data size, changes in allocation of resources). 2) the amortization of tuning costs and how this influences what workloads can be tuned in practice in a cost-effective manner. 3) the need for a comprehensive incremental tuning solution for a diverse set of workloads. We adapt different ML techniques in order to obtain efficient incremental tuning in our problem domain, and propose Tuneful, a configuration tuning framework. We show how it is designed to overcome the above issues and illustrate its applicability by running a wide array of experiments in cloud environments provided by two different service providers.
Markov-Based Transmission Power Control in Wireless Body Area Network

Reliability and energy consumption are the two important metrics in wireless body area network (WBAN) for medical application, and they are also the difficulties and key points in network research. In this article, we propose a new adaptive Markov-based transmission power control algorithm (MBPC) for a better compromise between energy consumption and reliability. The algorithm applies the Markov model to the link quality prediction using the grading strategy. Based on the prediction results, a new power adjustment strategy is proposed to extend the lifetime of the network while ensuring reliable transmission. In addition, we also incorporate open-loop thinking into the design of the algorithm to ensure the accuracy of the algorithm. The algorithm is simulated on the Castalia software platform. The simulation results show that the proposed algorithm has achieved good results in reliable transmission and energy consumption.
Hirnstruktur und Hirnfunktion: Die Rolle der Omega-3-Fettsäuren

Eine unzureichende Versorgung mit Eicosapentaen- (EPA) und Docosahexaensaure (DHA) pradisponiert wahrend der gesamten Lebensspanne zu suboptimaler Hirnstruktur und -funktion. Erhoht man die Zufuhr dieser Omega-3-Fettsauren gezielt auf Basis des HS-Omega-3-Index, konnen kognitive Fahigkeiten erhalten bzw. verbessert werden.
An early Sox2-dependent gene expression programme required for hippocampal dentate gyrus development

The hippocampus is a brain area central for cognition. Mutations in the human SOX2 transcription factor cause neurodevelopmental defects, leading to intellectual disability and seizures, together with hippocampal dysplasia. We generated an allelic series of Sox2 conditional mutations in mouse, deleting Sox2 at different developmental stages. Late Sox2 deletion (from E11.5, via Nestin-Cre) affects only postnatal hippocampal development; earlier deletion (from E10.5, Emx1-Cre) significantly reduces the dentate gyrus, and the earliest deletion (from E9.5, FoxG1-Cre) causes drastic abnormalities, with almost complete absence of the dentate gyrus. We identify a set of functionally interconnected genes (Gli3, Wnt3a, Cxcr4, p73 and Tbr2), known to play essential roles in hippocampal embryogenesis, which are downregulated in early Sox2 mutants, and (Gli3 and Cxcr4) directly controlled by SOX2; their downregulation provides plausible molecular mechanisms contributing to the defect. Electrophysiological studies of the Emx1Cre mouse model reveal altered excitatory transmission in CA1 and CA3 regions.
Detection of Ears of Rice in field Based on SSD

In order to improve the yield and quality of rice production, monitoring during rice development is particularly important. At present, rice pictures are recognized manually or by using traditional machine learning methods, but the efficiency is not high, and it is not possible to quickly identify rice in a wide range. Therefore, this paper studies the use of deep learning technology to identify rice ears, which can detect rice in the field environment faster and more accurately. In this paper, we used the SSD algorithm of deep learning object detection to train our own RICE dataset to obtain a rice ear detection model suitable for field environment feeding. The experimental results on the test set show that the mAP of this method can reach 38.1%, which provides a possibility for more in-depth research on neural network detection of rice, provides a basis for rice yield prediction, and better guides the production of rice crops.
End-to-End Image Reconstruction of Image from Human Functional Magnetic Resonance Imaging Based on the "Language" of Visual Cortex

In recent years, with the development of deep learning, the integration between neuroscience and computer vision has been deepened. In computer vision, it has been possible to generate images from text as well as semantic understanding from images based on deep learning. Here, text refers to human language, and the language that a computer can understand typically requires text to be encoded. In human brain visual expression, it also produces "descriptions" of visual stimuli, that is, the "language" that generates from the brain itself. Reconstruction of visual information is the process of reconstructing visual stimuli from the understanding of human brain, which is the most difficult to achieve in visual decoding. And based on the existing research of visual mechanisms, it is still difficult to understand the "language" of human brain. Inspired by generating images from text, we regarded voxel responses as the "language" of brain in order to reconstruct visual stimuli and built an end-to-end visual decoding model under the condition of small number of samples. We simply retrained a generative adversarial network (GAN) used to generate images from text on 1200 training data (including natural image stimuli and corresponding voxel responses). We regarded voxel responses as semantic information of brain, and sent them to GAN as prior information. The results showed that the decoding model we trained can reconstruct the natural images successfully. It also suggested the feasibility of reconstructing visual stimuli from "brain language", and the end-to-end model was more likely to learn the direct mapping between brain activity and visual perception. Moreover, it further indicated the great potential of combining neuroscience and computer vision.