Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

Aberrant regulation of FBW7 in cancer

FBW7 (F-box and WD repeat domain-containing 7) or Fbxw7 is a tumor suppressor, which promotes the ubiquitination and subsequent degradation of numerous oncoproteins including Mcl-1, Cyclin E, Notch, c- Jun, and c-Myc. In turn, FBW7 is regulated by multiple upstream factors including p53, C/EBP-δ, EBP2, Pin1, Hes-5 and Numb4 as well as by microRNAs such as miR-223, miR-27a, miR-25, and miR-129-5p. Given that the Fbw7 tumor suppressor is frequently inactivated or deleted in various human cancers, targeting FBW7 regulators is a promising anti-cancer therapeutic strategy

The Complex Function Method Roadway Section Design of the Soft Coal Seam

As for the sophisticated advanced support technique of vertical wall semicircle arch roadway in the three-soft coal seam, a design of flat top U-shape roadway section was put forward. Based on the complex function method, the surrounding rock displacement and stress distribution laws both of vertical wall semicircle arch roadway and of flat top U-shape roadway were obtained. The results showed that the displacement distribution laws in the edge of roadway surrounding rock were similar between the two different roadways and the area of plasticity proportion of flat top U-shape roadway approximately equals that of vertical wall semicircle arch roadway. Based on finite element method, the bearing behaviors of the U-type steel support under the interaction of surrounding rock in vertical wall semicircle arch roadway and flat top U-shape roadway were analyzed. The results showed that, from a mechanics perspective, U-type steel support can fulfill the requirement of surrounding rock supporting in flat top U-shape roadway and vertical wall semicircle arch roadway. The field measurement of mining roadway surrounding rock displacement in Zouzhuang coal mine working face 3204 verified the accuracy of theoretical analysis and numerical simulation

Boundary Guided Mixing Trajectory for Semantic Control with Diffusion Models

Applying powerful generative denoising diffusion models (DDMs) for downstream tasks such as image semantic editing usually requires either fine-tuning pre-trained DDMs or learning auxiliary editing networks. In this work, we achieve SOTA semantic control performance on various application settings by optimizing the denoising trajectory solely via frozen DDMs. As one of the first optimization-based diffusion editing work, we start by seeking a more comprehensive understanding of the intermediate high-dimensional latent spaces by theoretically and empirically analyzing their probabilistic and geometric behaviors in the Markov chain. We then propose to further explore the critical step in the denoising trajectory that characterizes the convergence of a pre-trained DDM. Last but not least, we further present our method to search for the semantic subspaces boundaries for controllable manipulation, by guiding the denoising trajectory towards the targeted boundary at the critical convergent step. We conduct extensive experiments on various DPMs architectures (DDPM, iDDPM) and datasets (CelebA, CelebA-HQ, LSUN-church, LSUN-bedroom, AFHQ-dog) with different resolutions (64, 256) as empirical demonstrations.Comment: 24 pages including appendices, code will be available at https://github.com/L-YeZhu/BoundaryDiffusio

Unseen Image Synthesis with Diffusion Models

While the current trend in the generative field is scaling up towards larger models and more training data for generalized domain representations, we go the opposite direction in this work by synthesizing unseen domain images without additional training. We do so via latent sampling and geometric optimization using pre-trained and frozen Denoising Diffusion Probabilistic Models (DDPMs) on single-domain datasets. Our key observation is that DDPMs pre-trained even just on single-domain images are already equipped with sufficient representation abilities to reconstruct arbitrary images from the inverted latent encoding following bi-directional deterministic diffusion and denoising trajectories. This motivates us to investigate the statistical and geometric behaviors of the Out-Of-Distribution (OOD) samples from unseen image domains in the latent spaces along the denoising chain. Notably, we theoretically and empirically show that the inverted OOD samples also establish Gaussians that are distinguishable from the original In-Domain (ID) samples in the intermediate latent spaces, which allows us to sample from them directly. Geometrical domain-specific and model-dependent information of the unseen subspace (e.g., sample-wise distance and angles) is used to further optimize the sampled OOD latent encodings from the estimated Gaussian prior. We conduct extensive analysis and experiments using pre-trained diffusion models (DDPM, iDDPM) on different datasets (AFHQ, CelebA-HQ, LSUN-Church, and LSUN-Bedroom), proving the effectiveness of this novel perspective to explore and re-think the diffusion models' data synthesis generalization ability.Comment: 28 pages including appendice

Outlier-Resilient Web Service QoS Prediction

The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates. Quality-of-Service (QoS) describes the non-functional characteristics of Web services, and it has become the key differentiator for service selection. However, users cannot invoke all Web services to obtain the corresponding QoS values due to high time cost and huge resource overhead. Thus, it is essential to predict unknown QoS values. Although various QoS prediction methods have been proposed, few of them have taken outliers into consideration, which may dramatically degrade the prediction performance. To overcome this limitation, we propose an outlier-resilient QoS prediction method in this paper. Our method utilizes Cauchy loss to measure the discrepancy between the observed QoS values and the predicted ones. Owing to the robustness of Cauchy loss, our method is resilient to outliers. We further extend our method to provide time-aware QoS prediction results by taking the temporal information into consideration. Finally, we conduct extensive experiments on both static and dynamic datasets. The results demonstrate that our method is able to achieve better performance than state-of-the-art baseline methods.Comment: 12 pages, to appear at the Web Conference (WWW) 202

Re-Attention Transformer for Weakly Supervised Object Localization

Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are mainly based on class activation map, which focuses on highlighting discriminative local region while ignoring the full object. In addition, the emerging transformer-based techniques constantly put a lot of emphasis on the backdrop that impedes the ability to identify complete objects. To address these issues, we present a re-attention mechanism termed token refinement transformer (TRT) that captures the object-level semantics to guide the localization well. Specifically, TRT introduces a novel module named token priority scoring module (TPSM) to suppress the effects of background noise while focusing on the target object. Then, we incorporate the class activation map as the semantically aware input to restrain the attention map to the target object. Extensive experiments on two benchmarks showcase the superiority of our proposed method against existing methods with image category annotations. Source code is available in \url{https://github.com/su-hui-zz/ReAttentionTransformer}.Comment: 11 pages, 5 figure

HGAttack: Transferable Heterogeneous Graph Adversarial Attack

Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is crucial. However, existing adversarial attack methods, which are primarily designed for homogeneous graphs, fall short when applied to HGNNs due to their limited ability to address the structural and semantic complexity of HGNNs. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs. We design a novel surrogate model to closely resemble the behaviors of the target HGNN and utilize gradient-based methods for perturbation generation. Specifically, the proposed surrogate model effectively leverages heterogeneous information by extracting meta-path induced subgraphs and applying GNNs to learn node embeddings with distinct semantics from each subgraph. This approach improves the transferability of generated attacks on the target HGNN and significantly reduces memory costs. For perturbation generation, we introduce a semantics-aware mechanism that leverages subgraph gradient information to autonomously identify vulnerable edges across a wide range of relations within a constrained perturbation budget. We validate HGAttack's efficacy with comprehensive experiments on three datasets, providing empirical analyses of its generated perturbations. Outperforming baseline methods, HGAttack demonstrated significant efficacy in diminishing the performance of target HGNN models, affirming the effectiveness of our approach in evaluating the robustness of HGNNs against adversarial attacks

Investigation of equilibrium and dynamic performance of SrCl2-expanded graphite composite in chemisorption refrigeration system

This work experimentally investigated adsorption equilibrium and reaction kinetics of ammonia adsorption/desorption on the composite of strontium chloride (SrCl2) impregnated into expanded graphite, and also discussed the potential influence of the addition of expanded graphite on the SrCl2-NH3 reaction characteristics. The measured and analysed results can be very useful information to design the system and operating conditions using the similar chemisorption composites. Equilibrium concentration characteristics of ammonia within the studied composite were measured using the heat sources at 90 °C, 100 °C and 110 °C for the decomposition process, where the degree of conversion achieved 50%, 78% and 96% respectively. Therefore, the equilibrium equation reflecting the relationship between temperature, pressure and concentration was developed, and a pseudo-equilibrium zone was found, which should be useful information to setup the system operating condition for the desired global transformation. It was suspected that the addition of expanded graphite altered the reaction equilibrium due to the pore effect and the salt-confinement. The concept of two-stage kinetic model was proposed and kinetic parameters were determined by fitting experimental data. The developed kinetic equations can predict dynamic cyclic performance of a reactive bed in similar geometric structure with reasonable accuracy. Such a chemisorption cycle using the SrCl2-expnaded graphite (mass ratio 2:1) composite can be used for cooling application, and the maximum SCP value can be achieved as high as 656 W/kg at t = 2.5 min, and the COP can be 0.3 after one hour of synthesis process under the condition of Tev = 0 °C, Tcon = 20 °C, Theat = 110 °C