Channel Cycle Time: A New Measure of Short-term Fairness

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical communication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors

AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

Balanced Order Batching with Task-Oriented Graph Clustering

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure

UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation

The rigid registration of aortic Digital Subtraction Angiography (DSA) and Computed Tomography Angiography (CTA) can provide 3D anatomical details of the vasculature for the interventional surgical treatment of conditions such as aortic dissection and aortic aneurysms, holding significant value for clinical research. However, the current methods for 2D/3D image registration are dependent on manual annotations or synthetic data, as well as the extraction of landmarks, which is not suitable for cross-modal registration of aortic DSA/CTA. In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning. Leveraging the imaging principles and characteristics of DSA and CTA, we have constructed a cross-dimensional registration environment based on spatial transformations. Specifically, we propose an overlap degree calculation reward function that measures the intensity difference between the foreground and background, aimed at assessing the accuracy of registration between segmentation maps and DSA images. This method is highly flexible, allowing for the loading of pre-trained models to perform registration directly or to seek the optimal spatial transformation parameters through online learning. We manually annotated 61 pairs of aortic DSA/CTA for algorithm evaluation. The results indicate that the proposed UDCR achieved a Mean Absolute Error (MAE) of 2.85 mm in translation and 4.35{\deg} in rotation, showing significant potential for clinical applications

Robust Representation Learning for Unified Online Top-K Recommendation

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD

sp-Carbon Incorporated Conductive Metal-Organic Framework as Photocathode for Photoelectrochemical Hydrogen Generation

Metal-organic frameworks (MOFs) have attracted increasing interest for broad applications in catalysis and gas separation due to their high porosity. However, the insulating feature and the limited active sites hindered MOFs as photocathode active materials for application in photoelectrocatalytic hydrogen generation. Herein, we develop a layered conductive two-dimensional conjugated MOF (2D c-MOF) comprising sp-carbon active sites based on arylene-ethynylene macrocycle ligand via CuO4 linking, named as Cu3HHAE2. This sp-carbon 2D c-MOF displays apparent semiconducting behavior and broad light absorption till the near-infrared band (1600 nm). Due to the abundant acetylene units, the Cu3HHAE2 could act as the first case of MOF photocathode for photoelectrochemical (PEC) hydrogen generation and presents a record hydrogen-evolution photocurrent density of ≈260 μA cm−2 at 0 V vs. reversible hydrogen electrode among the structurally-defined cocatalyst-free organic photocathodes

Eine sp-Kohlenstoffhaltige Leitfähige Metallorganische Gerüstverbindung als Photokathode für die Photoelektrochemische Wasserstoffentwicklung

Metallorganische Gerüstverbindungen (englisch metal–organic frameworks, MOFs) sind aufgrund ihrer hohen Porosität von großem Interesse für eine Vielzahl von Anwendungen in der Katalyse und Gastrennung. Eine begrenzte Anzahl an aktiven Zentren sowie das Verhalten als elektrischer Isolator machen den Einsatz von MOFs als aktives Photokathodenmaterial für die photoelektrokatalytische Wasserstoffproduktion allerdings nicht möglich. Wir berichten hiermit von der Entwicklung eines gestapelten, leitfähigen, zweidimensional-konjugierten MOFs (englisch 2D conjugated MOF, 2D c-MOF) welches aktive sp-Kohlenstoffzentren enthält. Der MOF Cu3HHAE2 basiert auf einem makrozyklischen Aryl-Alkin Liganden, welcher via CuO4 Einheiten verknüpft ist. Dieser sp-Kohlenstoff haltige 2D c-MOF zeigt Halbleitereigenschaften und eine breite Absorption bis in den nah-infraroten Bereich (1600 nm). Erstmalig kann dank der hohen Anzahl an Dreifachbindungen Cu3HHAE2 als MOF-Photokathode für die photoelektrochemische (PEC) Wasserstoffentwicklung verwendet werden. Verglichen mit anderen strukturell definierten, co-Katalysator freien organischen Photokathoden, zeigt er eine Rekordphotostromdichte für die Wasserstoffentwicklung von ≈ 260 μAcm⁻ ² bei 0 V gegen die reversible Wasserstoffelektrode (englisch reversible hydrogen electrode RHE)