We introduce Auto-Connect, a novel approach for automatic rigging that explicitly preserves skeletal connectivity through a connectivity-preserving tokenization scheme. Unlike previous methods that predict bone positions represented as two joints or first predict points before determining connectivity, our method employs special tokens to define endpoints for each joint's children and for each hierarchical layer, effectively automating connectivity relationships. This approach significantly enhances topological accuracy by integrating connectivity information directly into the prediction framework. To further guarantee high-quality topology, we implement a topology-aware reward function that quantifies topological correctness, which is then utilized in a post-training phase through reward-guided Direct Preference Optimization. Additionally, we incorporate implicit geodesic features for latent top-$k$ bone selection, which substantially improves skinning quality. By leveraging geodesic distance information within the model's latent space, our approach intelligently determines the most influential bones for each vertex, effectively mitigating common skinning artifacts. This combination of connectivity-preserving tokenization, reward-guided fine-tuning, and geodesic-aware bone selection enables our model to consistently generate more anatomically plausible skeletal structures with superior deformation properties.

Aiming to identify entity pairs with biomedical semantic relations and assign specific relation types, biomedical relation extraction (BioRE) plays a critical role in biomedical text mining and information extraction (IE). Recent studies indicate that general large language models (LLMs) have made some breakthroughs in general relation extraction (RE) tasks. However, even the advanced open-source LLMs struggle with BioRE tasks. For example, WizardLM-70B and LLaMA-2-70B achieve F-scores of 14.05 and 12.21 on the BioRED dataset, respectively, significantly lagging behind the state-of-the-art (SOTA) method which scores 65.17. To address this gap, a multitask instruction-tuning framework is proposed, which can transform general LLMs into BioRE-specialized models with our meticulously curated instruction dataset, REInstruct, comprising 150000 diverse and quality instruction-response pairs. Consequently, we introduce REaMA, a series of open-source LLMs with sizes of 7B and 13B specifically tailored for BioRE tasks. Experimental results on seven representative BioRE datasets show that both REaMA-2-7B and REaMA-2-13B acquire promising performance on all datasets. Remarkably, the larger REaMA-2-13B outperforms the current SOTA method on five out of seven datasets. The result exhibits the effectiveness of instruction-tuning on REInstruct in eliciting strong RE capabilities in LLMs. Furthermore, we show that incorporating chain of thought (CoT) into REInstruct can further enhance the generalization ability of REaMA. The project is available at https://github.com/stzpp/REaMA.

To exploit users’ heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this paper, we aim to understand the importance of data trading market (DTM) by studying the users’ operator selection and trading decisions, and analyzing the operator’s profit maximizing strategy. We model the interactions between the mobile operator and the users as a three-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its profit. In Stage II, each user chooses his operator. In Stage III, each DTM user chooses his trading decisions. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stages II and III, where every user proposes the same price such that the total demand matches with the total supply. We further show that the Stage I’s problem is convex and compute the optimal operation fee. Our analysis and numerical results show that an operator with a small initial market share can increase its profit by proposing a DTM, which is in line with the real-world situation in Hong Kong.

Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement in decision-making and automated control problems. As a result, we are witnessing a growing number of research works that are proposing ways of applying DRL techniques to network-related problems such as routing. However, such proposals failed to achieve good results, often under-performing traditional routing techniques. We argue that successfully applying DRL-based techniques to networking requires finding good representations of the network parameters: feature engineering. DRL agents need to represent both the state (e.g., link utilization) and the action space (e.g., changes to the routing policy). In this paper, we show that existing approaches use straightforward representations that lead to poor performance. We propose a novel representation of the state and action that outperforms existing ones and that is flexible enough to be applied to many networking use-cases. We test our representation in two different scenarios: (i) routing in optical transport networks and (ii) QoS-aware routing in IP networks. Our results show that the DRL agent achieves significantly better performance compared to existing state/action representations.

In conventional mobile data plans, the data is associated with a fixed period (e.g., one month) and the unused data will be cleared at the end of each period. To take advantage of consumers' heterogeneous demands across different periods and meanwhile to provide more time flexibility, some mobile data service providers (SP) have offered data plans with different lengths of period. In this paper, we consider the data plan design problem for a single SP, who provides data plans with different lengths of period for consumers with different characteristics of data demands. We propose a contract-theoretic approach, wherein the SP offers a period-price data plan contract which consists of a set of period and price combinations, indicating the prices for data with different periods. We study the optimal data plan contract designs under two different models: discrete and continuous consumer-type models, depending on whether the consumer type is discrete or continuous. In the former model, each type of consumers are assigned with a specific period-price combination. In the latter model, the consumers are first categorized into a finite number of groups, and each group of consumers (possibly with different types) are assigned with a specific period-price combination. We systematically analyze the incentive compatibility (IC) constraint and individual rationality (IR) constraint, which ensure each consumer to choose the data plan with the period-price combination intended for his type. We further derive the optimal contract that maximizes the SP's expected profit, meanwhile satisfying the IC and IR constraints of consumers. Our numerical results show that our proposed optimal contract can increase the SP's profit over 35% comparing with the conventional monthly-period data plan.

To exploit users' heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this work, we aim to understand the users' optimal trading decisions and the operator's revenue maximizing strategy. We model the interactions between the mobile operator and the users as a two-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its revenue. In Stage II, each user decides whether to be a seller or a buyer and optimizes the corresponding trading price and quantity. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stage II in closed-form, and prove that the users' decisions can converge to the NE through distributed best response updates. We show that at the NE, different types of sellers and buyers should propose the same price such that the total demand matches the total supply. We further show that the Stage I operation fee optimization problem is convex, and derive the optimal operation fee in closed-form. Our analysis and numerical results show that the users who have less uncertainty of their data usages can benefit more from data trading. We also show that an operation fee that is too high hurts both the users' payoffs and the operator's revenue.

Motivated by the recently launched mobile data trading markets (e.g., China Mobile Hong Kong's 2nd exChange Market), in this paper, the mobile data trading problem is studied under future data demand uncertainty. A brokerage-based market is introduced, in which sellers and buyers propose their selling and buying quantities, respectively, to the trading platform that matches the market supply and demand. To understand the users' realistic trading behaviors, a prospect theory (PT) model from behavioral economics is proposed, which includes the widely adopted expected utility theory (EUT) as a special case. Although the PT modeling leads to a challenging non-convex optimization problem, the optimal solution can be characterized by exploiting the unimodal structure of the objective function. Building upon this analysis, an algorithm is designed to help estimate the users' risk preference and provide trading recommendations dynamically, considering the latest market and usage information. It is shown via simulations that the risk preferences have a significant impact on a user's decision and outcome: a risk-averse dominant user can guarantee a higher minimum profit in the trading, while a risk-seeking dominant user can achieve a higher maximum profit. By comparing with the EUT benchmark, it is shown that a PT user with a low reference point is more willing to buy mobile data. Moreover, compared with an EUT user, a PT user is more willing to buy mobile data when the probability of large data demand is low.

In this paper, we study a virtual wireless operator's spectrum investment problem under spectrum supply uncertainty. To obtain enough spectrum resources to meet its customer demands, the virtual operator can either sense for the temporarily unused spectrum in a licensed band or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of available spectrum obtained by sensing is uncertain due to the primary users' activities in the licensed band. Previous studies on spectrum investment problems mainly considered the expected profit maximization problem of a risk-neutral operator based on the expected utility theory (EUT). In reality, however, an operator's decision is influenced by not only the consideration of expected profit maximization, but also the level of its risk preference. To capture this tradeoff between these two considerations, we analyze the operator's optimal decision problem using the prospect theory from behavioral economics, which includes EUT as a special case. The sensing and leasing optimal problem under prospect theory is non-convex and challenging to solve. Nevertheless, by exploiting the unimodal structure of the problem, we are able to compute the unique global optimal solution. We show that, compared with an EUT operator, both the risk-averse and risk-seeking operator achieve a smaller expected profit. On the other hand, a risk-averse operator can guarantee a larger minimum possible profit, while a risk-seeking operator can achieve a larger maximum possible profit. Furthermore, the tradeoff between the expected profit and the minimum possible profit for a risk-averse operator is better when the sensing cost increases, while the tradeoff between the expected profit and the maximum possible profit for a risk-seeking operator is better when the sensing cost decreases.

Motivated by the recently launched 2CM data trading platform of China Mobile Hong Kong, we study the optimal user mobile data trading problem under the future demand uncertainty. We consider a brokerage-based market, where sellers and buyers propose their selling and buying prices and quantities to the trading platform, respectively. The platform acts as a broker, which facilitates the trade by matching the supply and demand. To understand users' realistic trading behaviors, we use prospect theory (PT) from behavioral economics in the modeling, which leads to a challenging non-convex optimization problem. Nevertheless, we are able to determine the unique optimal solution in closed-form, by utilizing the unimodal structure of the objective function. When comparing with the benchmark expected utility theory (EUT), we show that a PT user with a low reference point is more willing to buy mobile data. Moreover, when the probability of high demand is low, comparing with an EUT user, a PT user is more willing to buy mobile data due to the probability distortion.

We study a secondary wireless operator's spectrum investment problem under spectrum supply uncertainty using prospect theory. In order to meet the demands of its users, the secondary operator can either sense for the unused spectrum in a licensed band, or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of spectrum obtained by sensing is uncertain. We formulate such a hybrid spectrum investment problem as a two-stage optimization problem, and compute the optimal sensing and leasing decisions using backward induction. To model the realistic investment behaviors under uncertainty, we apply prospect theory to overcome the limitations of the widely adopted expected utility theory. We show that the investment decision model based on prospect theory leads to a non-convex optimization problem, which is challenging to solve in closed-form. However, we characterize the uniqueness of the optimal solution analytically, and compute it through a simple line search. Comparing with the expected utility theory benchmark, the analysis based on prospect theory shows that the operator will be more conservative in sensing, in order to reduce the investment risk and to avoid a large possible loss. In other words, the operator is both risk averse and loss averse.