Miracle-dz/UniFAR-FTUs huggingface dataset:
MGNN: Graph Neural Networks Inspired by Distance Geometry Problem

Graph Neural Networks (GNNs) have emerged as a prominent research topic in the field of machine learning. Existing GNN models are commonly categorized into two types: spectral GNNs, which are designed based on polynomial graph filters, and spatial GNNs, which utilize a message-passing scheme as the foundation of the model. For the expressive power and universality of spectral GNNs, a natural approach is to improve the design of basis functions for better approximation ability. As for spatial GNNs, models like Graph Isomorphism Networks (GIN) analyze their expressive power based on Graph Isomorphism Tests. Recently, there have been attempts to establish connections between spatial GNNs and geometric concepts like curvature and cellular sheaves, as well as physical phenomena like oscillators. However, despite the recent progress, there is still a lack of comprehensive analysis regarding the universality of spatial GNNs from the perspectives of geometry and physics. In this paper, we propose MetricGNN (MGNN), a spatial GNN model inspired by the congruent-insensitivity property of classifiers in the classification phase of GNNs. We demonstrate that a GNN model is universal in the spatial domain if it can generate embedding matrices that are congruent to any given embedding matrix. This property is closely related to the Distance Geometry Problem (DGP). Since DGP is an NP-Hard combinatorial optimization problem, we propose optimizing an energy function derived from spring networks and the Multi-Dimensional Scaling (MDS) problem. This approach also allows our model to handle both homophilic and heterophilic graphs. Finally, we propose employing the iteration method to optimize our energy function. We extensively evaluate the effectiveness of our model through experiments conducted on both synthetic and real-world datasets. Our code is available at: https://github.com/GuanyuCui/MGNN.
Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation

Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.
Generative Causal Interpretation Model for Spatio-Temporal Representation Learning

Learning, interpreting, and predicting from complex and high-dimensional spatio-temporal data is a natural ability of humans and other intelligent agents, and one of the most important and difficult challenges of AI. Although objects may present different observed phenomena under different situations, their causal mechanism and generation rules are stable and invariant. Different from most existing studies that focus on dynamic correlation, we explore the latent causal structure and mechanism of causal descriptors in the spatio-temporal dimension at the microscopic level, thus revealing the generation principle of observation. In this paper, we regard the causal mechanism as a spatio-temporal causal process modulated by non-stationary exogenous variables. To this end, we propose a theoretically-grounded Generative Causal Interpretation Model (GCIM), which infers explanatory-capable microscopic causal descriptors from observational data via spatio-temporal causal representations. The core of GCIM is to estimate the prior distribution of causal descriptors by using the spatio-temporal causal structure and transition process under the constraints of identifiable conditions, thus extending the Variational Auto Encoder (VAE). Furthermore, our method is able to automatically capture domain information from observations to model non-stationarity. We further analyze the model identifiability, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments on synthetic and real-world datasets show that GCIM can successfully identify latent causal descriptors and structures, and accurately predict future data.
The Role of Social Media Analytics in Providing Product Intelligence: A Qualitative Study

The use of social media for business is becoming a new norm for gaining business intelligence. While research work is increasing on social media analytics (SMA) for some aspects of business intelligence, the knowledge of how SMA facilitates product intelligence (PI) is still very limited. To address the gap, the data from qualitative interviews was analyzed using NVivo coding and matrix queries. The results show that products, processes, and people underpin the mechanism for providing SMA-enabled PI. Serving dichotomous objectives, people act as (1) conduits for providing insights about products and processes and (2) subjects for gathering behavioral insights. The PI gathered about the products could help understanding of the process effectiveness and be used for planning, product, and promotional strategy development. Because of the impulsive nature of SM, while the utility of PI from SMA may be limited to gaining short-to-medium-term benefits, it is an important indicator of customer and market sentiments. Theoretically, the study develops a framework of SMA-enabled PI, highlighting the mechanism that yields PI and advancing knowledge on the role of SMA in improving business efficiencies. The results provide managerial insights about the levers and limitations to guide the design of SMA strategies for acquiring PI.
Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing Systems

This paper explores parallel computing systems for efficient subgraph query processing in large graphs. We investigate how to take advantage of the inherent parallelism of parallel computing systems for both intraquery and interquery optimization during subgraph query processing. Rather than relying on widely-used hash-based methods, we utilize and extend locality sensitive hashing methods. For intraquery optimization, we use the structures of both the data graph and subgraph query to design a query-constraint locality sensitive hashing method named QCMH, which can be used to merge multiple tasks during a single subgraph query processing. For interquery optimization, we propose a query locality sensitive hashing method named QMH, which can be used to detect common subgraphs among different subgraph queries, thereby merging multiple subgraph queries. Our proposed methods can reduce the redundant computation among multiple tasks duringa single subgraph query processing or multiple queries. Extensive experimental studies on large real and synthetic graphs show that our proposed methods can improve query performance compared to state-of-the-art methods by 10% to 50%.
Efficient Continuous Space Policy Optimization for High-frequency Trading

High-frequency trading is an extraordinarily intricate financial task, which is normally treated as a near real-time sequential decision problem. Compared with the traditional two-phase approach, forecasting equity's trend and then weighting them by combinatorial optimization, deep reinforcement learning (DRL) methods have shown advances in reward chasing with optimal policies. However, existing DRL-based methods either leverage portfolio optimization on low-frequency scenarios or only support a very limited number of assets with discrete action space, facing significant computing efficiency challenges. Therefore, we propose an efficient DRL-based policy optimization (DRPO) method for high-frequency trading. In particular, we model the portfolio management task with Markov Decision Process by directly inferring the equity weights in the action space guided by maximum accumulated returns. To reduce agents' interaction complexity without reducing interpretation, we detach the environment into the "static'' market states and "dynamic'' portfolio weight states. Then, we design an efficient reward expectation calculation algorithm via probabilistic dynamic programming, which enables our agents directly collect feedback away from trajectory sampling-based morass. To the best of our knowledge, this is the first work that solves the high-frequency portfolio optimization problem by devising an efficient continuous space policy optimization algorithm in the DRL framework. Through extensive experiments on the real-world data from Dow Jones, Coinbase and SSE exchanges, we show that our proposed DRPO significantly outperforms state-of-the-art benchmark methods. The results demonstrate the practical applicability and effectiveness of the proposed method.
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make recommendations. Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. Using this simulator we develop and train a modified Deep Q-Network, the action head DQN (AH-DQN), in a manner that addresses the challenges imposed by the large state and action space of our RL formulation. The resulting policy is capable of making recommendations from large and dynamic sets of candidate items with the expectation of maximizing consumption metrics. We analyze and evaluate agents offline via simulations that use environment models trained on both public and proprietary streaming datasets. We show how these agents lead to better user-satisfaction metrics compared to baseline methods during online A/B tests. Finally, we demonstrate that performance assessments produced from our simulator are strongly correlated with observed online metric results.
Graph Neural Networks in TensorFlow

Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. In this tutorial we'll cover essential applications of Graph Machine Learning using TensorFlow GNN1[12], a Python framework that extends TensorFlow [1] with Graph Neural Networks (GNNs): models that leverage graph-structured data. TF-GNN is motivated and informed by years of applying graph representation learning to practical problems at Google [2-8, 10, 11, 13-19, 22-29]. In particular, TF-GNN focuses on the representation of heterogeneous graph data and supports the explicit modeling of an arbitrary number of relationship (edge) types between an arbitrary number of entity (node) types. These relationships can be used in combination with other TensorFlow components, e.g., a TF-GNN model might connect representations from a language model to those of a vision model and fine-tune these features for a node classification task. Many teams at Google run TF-GNN models in production. We believe this to be a direct consequence of TF-GNN's multi-layered API which is designed for accessibility to developers (regardless of their prior experience with machine learning).
Distributionally-Informed Recommender System Evaluation

Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty. In this paper, we argue for the need for researchers and practitioners to attend more closely to various distributions that arise from a recommender system (or other information access system) and the sources of uncertainty that lead to these distributions. One immediate implication of our argument is that both researchers and practitioners must report and examine more thoroughly the distribution of utility between and within different stakeholder groups. However, distributions of various forms arise in many more aspects of the recommender systems experimental process, and distributional thinking has substantial ramifications for how we design, evaluate, and present recommender systems evaluation and research results. Leveraging and emphasizing distributions in the evaluation of recommender systems is a necessary step to ensure that the systems provide appropriate and equitably-distributed benefit to the people they affect.
A Data-Driven Decision Support Framework for Player Churn Analysis in Online Games

Faced with saturated market and fierce competition of online games, it is of great value to analyze the causes of the player churn for improving the game product, maintaining the player retention. A large number of research efforts on churn analysis have been made into churn prediction, which can achieve a sound accuracy benefiting from the booming of AI technologies. However, game publishers are usually unable to apply high-accuracy prediction methods in practice for preventing or relieving the churn due to the lack of the specific decision support (e.g., why they leave and what to do next). In this study, we fully exploit the expertise in online games and propose a comprehensive data-driven decision support framework for addressing game player churn. We first define the churn analysis in online games from a commercial perspective and elaborate the core demands of game publishers for churn analysis. Then we employ and improve the cutting-edge eXplainable AI (XAI) methods to predict player churn and analyze the potential churn causes. The possible churn causes can finally guide game publishers to make specific decisions of revision or intervention in our designed procedure. We demonstrate the effectiveness and high practical value of the framework by conducting extensive experiments on a real-world large-scale online game, Justice PC. The whole decision support framework, bringing interesting and valuable insights, also receives quite positive reviews from the game product and operation teams. Notably, the whole pipeline is readily transplanted to other online systems for decision support to address similar issues.
MIR319C and its target JAW-TCPs repress each other and establish cell proliferation pattern in incipient leaf primordia in Arabidopsis thaliana

The microRNA miR319 and its target JAW-TCP transcription factors regulate leaf morphogenesis in diverse plant species. In young Arabidopsis leaf primordia, JAW-TCPs are detected towards the distal region whereas MIR319C is expressed at the base. Little is known about how this complementary expression pattern of MIR319C and JAW-TCPs is generated. Here, we show that MIR319C is initially expressed uniformly throughout the incipient primordia and is later abruptly down-regulated at the distal region, with concomitant distal appearance of JAW-TCPs, when leaves grow to ∼100 µm long. Loss of JAW-TCPs causes distal extension of MIR319C expression domain, whereas ectopic TCP activity restricts MIR319C more proximally. JAW-TCPs are recruited to and are capable of depositing histone H3K27me3 repressive marks on the MIR319C chromatin. JAW-TCPs fail to repress MIR319C in transgenic seedlings where the TCP-binding cis-elements on MIR319C are mutated, causing miR319 gain-of-function-like phenotype. Based on these results, we propose a model for growth patterning in leaf primordia wherein MIR319C and JAW-TCPs repress each other and divide the uniformly growing primordia into distal differentiation zone and proximal proliferation domain. Summary statement JAW-TCPs transcriptionally repress the microRNA319 encoding gene MIR319C to generate their mutually exclusive expression pattern and establish growth polarity during early stages of Arabidopsis leaf primordia.
Deep Weakly-supervised Anomaly Detection

Anomaly detection is typically posited as an unsupervised learning task in the literature due to the prohibitive cost and difficulty to obtain large-scale labeled anomaly data, but this ignores the fact that a very small number (e.g.,, a few dozens) of labeled anomalies can often be made available with small/trivial cost in many real-world anomaly detection applications. To leverage such labeled anomaly data, we study an important anomaly detection problem termed weakly-supervised anomaly detection, in which, in addition to a large amount of unlabeled data, a limited number of labeled anomalies are available during modeling. Learning with the small labeled anomaly data enables anomaly-informed modeling, which helps identify anomalies of interest and address the notorious high false positives in unsupervised anomaly detection. However, the problem is especially challenging, since (i) the limited amount of labeled anomaly data often, if not always, cannot cover all types of anomalies and (ii) the unlabeled data is often dominated by normal instances but has anomaly contamination. We address the problem by formulating it as a pairwise relation prediction task. Particularly, our approach defines a two-stream ordinal regression neural network to learn the relation of randomly sampled instance pairs, i.e., whether the instance pair contains two labeled anomalies, one labeled anomaly, or just unlabeled data instances. The resulting model effectively leverages both the labeled and unlabeled data to substantially augment the training data and learn well-generalized representations of normality and abnormality. Comprehensive empirical results on 40 real-world datasets show that our approach (i) significantly outperforms four state-of-the-art methods in detecting both of the known and previously unseen anomalies and (ii) is substantially more data-efficient.
WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis

Given its broad applications, time series analysis has gained substantial research attention but remains a very challenging task. Recent years have witnessed the great success of deep learning methods, eg. , CNN and RNN, in time series classification and forecasting, but heterogeneity as the very nature of time series has not yet been addressed adequately and remains the performance "treadstone. " In this light, we argue that the intra-sequence non-stationarity and inter-sequence asynchronism are two types of heterogeneities widely existed in multiple times series, and propose a hybrid attention network called WHEN as deep learning solution. WHEN features in two attention mechanisms in two different modules. In the WaveAtt module, we propose a novel data-dependent wavelet function and integrate it into the BiLSTM network as the wavelet attention, for the purpose of analyzing dynamic frequency components in nonstationary time series. In the DTWAtt module, we transform the dynamic time warping (DTW) technique into the form as the DTW attention, where all input sequences are synchronized with a universal parameter sequence to overcome the time distortion problem in multiple time series. WHEN with the hybrid attentions is then formulated as task-dependent neural network for either classification or forecasting tasks. Extensive experiments on 30 UEA datasets and 3 real-world datasets with rich competitive baselines demonstrate the excellent performance of our model. The ability of WHEN in dealing with time series heterogeneities is also elaborately explored via specially designed analysis.
PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at Alipay

Linear programming (LP) is arguably the most common optimization problem encountered in practical settings. Important examples include machine learning systems optimization, resource allocation, and other decision-making scenarios. However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. PDAS offers user-friendly interfaces and provides near-linear speedup thanks to its high scalability and excellent performance. It also comes with a failover mechanism to ensure the stability of the iterative process. The convergence, feasibility, and optimality of PDAS have been verified on two real-world data-sets, resulting in a 10-4 average relative deviation from Gurobi. Although SOTA solvers do have advantages if only considering the solving time when tested on five small and medium-sized public data-sets, PDAS is more promising after including the modeling time. Moreover, when used to solve large-scale LP problems with up to 109 decision variables and 104 constraints in three real-world scenarios, PDAS achieves at least 2x speedups, well beyond the capabilities of SOTA.
Tmem263 deletion disrupts the GH/IGF-1 axis and causes dwarfism and impairs skeletal acquisition

Genome-wide association studies (GWAS) have identified a large number of candidate genes believed to affect longitudinal bone growth and bone mass. One of these candidate genes, TMEM263, encodes a poorly characterized plasma membrane protein. Single nucleotide polymorphisms in TMEM263 are associated with bone mineral density in humans and mutations are associated with dwarfism in chicken and severe skeletal dysplasia in at least one human fetus. Whether this genotype-phenotype relationship is causal, however, remains unclear. Here, we determine whether and how TMEM263 is required for postnatal growth. Deletion of the Tmem263 gene in mice causes severe postnatal growth failure, proportional dwarfism, and impaired skeletal acquisition. Mice lacking Tmem263 show no differences in body weight within the first two weeks of postnatal life. However, by P21 there is a dramatic growth deficit due to a disrupted GH/IGF-1 axis, which is critical for longitudinal bone growth. Tmem263-null mice have low circulating IGF-1 levels and pronounced reductions in bone mass and growth plate length. The low serum IGF-1 in Tmem263-null mice is associated with reduced hepatic GH receptor (GHR) expression and GH-induced JAK2/STAT5 signaling. A deficit in GH signaling dramatically alters GH-regulated genes and feminizes the liver transcriptome of Tmem263-null male mice, with their expression profile resembling a wild-type female, hypophysectomized male, and Stat5b-null male mice. Collectively, our data validates the causal role for Tmem263 in regulating postnatal growth and raises the possibility that rare mutations or variants of TMEM263 may potentially cause GH insensitivity and impair linear growth.