Analysis of Mobile Game Intellectual Property Marketing Strategy: A Case Study of Honor of Kings

The mobile game market is now gradually forming a unique ecological industry chain. Game parties are beginning to look beyond the game experience and focus on building mature game IPs. By creating specific IP to drive the game’s peripheral revenue, strengthen the emotional connection with game users, and achieve the purpose of long-term development. Looking at the domestic market, Honor of Kings, as a phenomenal mobile game in China, its IP development and marketing are of reference learning significance. In this paper, we selected Honor of Kings as the research object, and we collected data through both questionnaire surveys and interviews, using SPSS for statistical analysis. The research analyzed its IP marketing strategy and effect and searched for the factors which affect its IP marketing effect. It finds that the impact of Honor of Kings IP marketing is influenced by the degree of perfection of Honor of Kings worldview, i.e., IP connotation and local cultural awareness. At the same time, we analyzed the IP development process and marketing strategy of Honor of Kings in combination, pointed out its advantages and shortcomings, and gave suggestions to provide new ideas for IP marketing of other game companies

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power

The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of tasks. It has been recently used as a popular metric for evaluating the expressive power of GNNs. Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i.e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph. However, those methods require heavy preprocessing, and suffer from high time and memory costs. In this paper, we overcome the aforementioned limitations of subgraph GNNs by proposing a novel class of GNNs -- $d$-Distance-Restricted FWL(2) GNNs, or $d$-DRFWL(2) GNNs. $d$-DRFWL(2) GNNs use node pairs whose mutual distances are at most $d$ as the units for message passing to balance the expressive power and complexity. By performing message passing among distance-restricted node pairs in the original graph, $d$-DRFWL(2) GNNs avoid the expensive subgraph extraction operations in subgraph GNNs, making both the time and space complexity lower. We theoretically show that the discriminative power of $d$-DRFWL(2) GNNs strictly increases as $d$ increases. More importantly, $d$-DRFWL(2) GNNs have provably strong cycle counting power even with $d=2$: they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene rings) are ubiquitous in organic molecules, being able to detect and count them is crucial for achieving robust and generalizable performance on molecular tasks. Experiments on both synthetic datasets and molecular datasets verify our theory. To the best of our knowledge, our model is the most efficient GNN model to date (both theoretically and empirically) that can count up to 6-cycles

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

Self-supervised pre-training recently demonstrates success on large-scale multimodal data, and state-of-the-art contrastive learning methods often enforce the feature consistency from cross-modality inputs, such as video/audio or video/text pairs. Despite its convenience to formulate and leverage in practice, such cross-modality alignment (CMA) is only a weak and noisy supervision, since two modalities can be semantically misaligned even they are temporally aligned. For example, even in the commonly adopted instructional videos, a speaker can sometimes refer to something that is not visually present in the current frame; and the semantic misalignment would only be more unpredictable for the raw videos from the internet. We conjecture that might cause conflicts and biases among modalities, and may hence prohibit CMA from scaling up to training with larger and more heterogeneous data. This paper first verifies our conjecture by observing that, even in the latest VATT pre-training using only instructional videos, there exist strong gradient conflicts between different CMA losses within the same video, audio, text triplet, indicating them as the noisy source of supervision. We then propose to harmonize such gradients, via two techniques: (i) cross-modality gradient realignment: modifying different CMA loss gradients for each sample triplet, so that their gradient directions are more aligned; and (ii) gradient-based curriculum learning: leveraging the gradient conflict information on an indicator of sample noisiness, to develop a curriculum learning strategy to prioritize training on less noisy sample triplets. Applying those techniques to pre-training VATT on the HowTo100M dataset, we consistently improve its performance on different downstream tasks. Moreover, we are able to scale VATT pre-training to more complicated non-narrative Youtube8M dataset to further improve the state-of-the-arts.Comment: Accepted at NeurIPS 202

Efficient Information-Theoretic Distributed Point Function with General Output Groups

An $n$-server information-theoretic \textit{Distributed Point Function} (DPF) allows a client to secret-share a point function $f_{\alpha,\beta}(x)$ with domain $[N]$ and output group $\mathbb{G}$ among $n$ servers such that each server learns no information about the function from its share (called a key) but can compute an additive share of $f_{\alpha,\beta}(x)$ for any $x$. DPFs with small key sizes and general output groups are preferred. In this paper, we propose a new transformation from share conversions to information-theoretic DPFs. By applying it to share conversions from Efremenko\u27s PIR and Dvir-Gopi PIR, we obtain both an 8-server DPF with key size $O(2^{10\sqrt{\log N\log\log N}}+\log p)$ and output group $\mathbb{Z}_p$ and a 4-server DPF with key size $O(\tau \cdot 2^{6\sqrt{\log N\log\log N}})$ and output group $\mathbb{Z}_{2^\tau}$. The former allows us to partially answer an open question by Boyle, Gilboa, Ishai, and Kolobov (ITC 2022) and the latter allows us to build the first DPFs that may take any finite Abelian groups as output groups. We also discuss how to further reduce the key sizes by using different PIR, how to reduce the number of servers by resorting to statistical security or using nice integers, and how to obtain DPFs with $t$-security. We show the applications of the new DPFs by constructing new efficient PIR protocols with result verification

Evaluation of installation timing of initial ground support for large-span tunnel in hard rock

In conventional drill and blast tunnelling, initial ground support is placed immediately after the current round is shot before excavation of the next round (i.e. one-round installation method). When tunnelling in hard rock, one-round installation of initial ground support conservatively ensures tunnel integrity, but meanwhile brings up other problems such as over-break at tunnel face, slow excavation rate and so forth. In this study, a large-span tunnel in Class III hard rock was monitored by a network of sensors to investigate tunnel internal forces in three construction scenarios where initial ground supports were placed in different timing and sequence: (1) initial ground support installed immediately after current round (2) support installed after two rounds (3) support installed after three consecutive rounds. The collected field measurements together with construction records were evaluated from three aspects: structural stability, constructability and cost-effectiveness. Results show that the installation of initial ground support after two rounds generally led to the most regular and minimum tunnel internal forces of the three construction scenarios, whilst it managed to minimize under & over-break and allow more space for construction convenience. In the meanwhile, this installation sequence significantly accelerated tunnel advance rate at lower material cost

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class citizen. Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. Experimental results show that TensorIR compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state-of-art hand-optimized systems across platforms.Comment: Accepted to ASPLOS 202

IKKβ Regulates the Repair of DNA Double-Strand Breaks Induced by Ionizing Radiation in MCF-7 Breast Cancer Cells

Activation of the IKK-NFκB pathway increases the resistance of cancer cells to ionizing radiation (IR). This effect has been largely attributed to the induction of anti-apoptotic proteins by NFκB. Since efficient repair of DNA double strand breaks (DSBs) is required for the clonogenic survival of irradiated cells, we investigated if activation of the IKK-NFκB pathway also regulates DSB repair to promote cell survival after IR. We found that inhibition of the IKK-NFκB pathway with a specific IKKβ inhibitor significantly reduced the repair of IR-induced DSBs in MCF-7 cells. The repair of DSBs was also significantly inhibited by silencing IKKβ expression with IKKβ shRNA. However, down-regulation of IKKα expression with IKKα shRNA had no significant effect on the repair of IR-induced DSBs. Similar findings were also observed in IKKα and/or IKKβ knockout mouse embryonic fibroblasts (MEFs). More importantly, inhibition of IKKβ with an inhibitor or down-regulation of IKKβ with IKKβ shRNA sensitized MCF-7 cells to IR-induced clonogenic cell death. DSB repair function and resistance to IR were completely restored by IKKβ reconstitution in IKKβ-knockdown MCF-7 cells. These findings demonstrate that IKKβ can regulate the repair of DSBs, a previously undescribed and important IKKβ kinase function; and inhibition of DSB repair may contribute to cance cell radiosensitization induced by IKKβ inhibition. As such, specific inhibition of IKKβ may represents a more effective approach to sensitize cancer cells to radiotherapy

Predicting tonal realizations in one Chinese dialect from another

Theoretical and Experimental Linguistic