Essays In Mechanism Design

In this thesis, I study mechanism design problems in environments where the information necessary to make decisions is affected by the actions of principal or agents. The first chapter considers the problem of a principal who must allocate a good among a finite number of agents, each of whom values the good. Each agent has private information about the principal\u27s payoff if he receives the good. There are no monetary transfers. The principal can inspect agents\u27 reports at a cost and punish them, but punishments are limited because verification is imperfect or information arrives only after the good has been allocated for a while. I characterize an optimal mechanism featuring two thresholds. Agents whose values are below the lower threshold and above the upper threshold are pooled, respectively. If the number of agents is small, then the pooling area at the top of value distribution disappears. If the number of agents is large, then the two pooling areas meet and the optimal mechanism can be implemented via a shortlisting procedure. The fact that the optimal mechanism depends on the number of agents implies that small and large organizations should behave differently. The second chapter considers the problem of a principal who wishes to distribute an indivisible good to a population of budget-constrained agents. Both valuation and budget are an agent\u27s private information. The principal can inspect an agent\u27s budget through a costly verification process and punish an agent who makes a false statement. I characterize the direct surplus-maximizing mechanism. This direct mechanism can be implemented by a two-stage mechanism in which agents only report their budgets. Specifically, all agents report their budgets in the first stage. The principal then provides budget-dependent cash subsidies to agents and assigns the goods randomly (with uniform probability) at budget-dependent prices. In the second stage, a resale market opens, but is regulated with budget-dependent sales taxes. Agents who report low budgets receive more subsidies in their initial purchases (the first stage), face higher taxes in the resale market (the second stage) and are inspected randomly. This implementation exhibits some of the features of some welfare programs, such as Singapore\u27s housing and development board. The third chapter studies the design of ex-ante efficient mechanisms in situations where a single item is for sale, and agents have positively interdependent values and can covertly acquire information at a cost before participating in a mechanism. I find that when interdependency is low or the number of agents is large, the ex-post efficient mechanism is also ex-ante efficient. In cases of high interdependency or a small number of agents, ex-ante efficient mechanisms discourage agents from acquiring excessive information by introducing randomization to the ex-post efficient allocation rule in areas where the information\u27s precision increases most rapidly

Video Question Answering via Attribute-Augmented Attention Network Learning

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.Comment: Accepted for SIGIR 201

Suppressing Instability in a Vlasov-Poisson System by an External Electric Field Through Constrained Optimization

Fusion energy offers the potential for the generation of clean, safe, and nearly inexhaustible energy. While notable progress has been made in recent years, significant challenges persist in achieving net energy gain. Improving plasma confinement and stability stands as a crucial task in this regard and requires optimization and control of the plasma system. In this work, we deploy a PDE-constrained optimization formulation that uses a kinetic description for plasma dynamics as the constraint. This is to optimize, over all possible controllable external electric fields, the stability of the plasma dynamics under the condition that the Vlasov--Poisson (VP) equation is satisfied. For computing the functional derivative with respect to the external field in the optimization updates, the adjoint equation is derived. Furthermore, in the discrete setting, where we employ the semi-Lagrangian method as the forward solver, we also explicitly formulate the corresponding adjoint solver and the gradient as the discrete analogy to the adjoint equation and the Frechet derivative. A distinct feature we observed of this constrained optimization is the complex landscape of the objective function and the existence of numerous local minima, largely due to the hyperbolic nature of the VP system. To overcome this issue, we utilize a gradient-accelerated genetic algorithm, leveraging the advantages of the genetic algorithm's exploration feature to cover a broader search of the solution space and the fast local convergence aided by the gradient information. We show that our algorithm obtains good electric fields that are able to maintain a prescribed profile in a beam shaping problem and uses nonlinear effects to suppress plasma instability in a two-stream configuration

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like Resnet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called dynamic-static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https://github.com/zhoubenjia/RAAR3DNet.Comment: Accepted by AAAI 202

Safe Reinforcement Learning with Dual Robustness

Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either address safety requirements under the assumption of no adversary (e.g., safe RL) or only focus on robustness against performance adversaries (e.g., robust RL). Learning one policy that is both safe and robust remains a challenging open problem. The difficulty is how to tackle two intertwined aspects in the worst cases: feasibility and optimality. Optimality is only valid inside a feasible region, while identification of maximal feasible region must rely on learning the optimal policy. To address this issue, we propose a systematic framework to unify safe RL and robust RL, including problem formulation, iteration scheme, convergence analysis and practical algorithm design. This unification is built upon constrained two-player zero-sum Markov games. A dual policy iteration scheme is proposed, which simultaneously optimizes a task policy and a safety policy. The convergence of this iteration scheme is proved. Furthermore, we design a deep RL algorithm for practical implementation, called dually robust actor-critic (DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC achieves high performance and persistent safety under all scenarios (no adversary, safety adversary, performance adversary), outperforming all baselines significantly

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy

Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States

Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrary given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved. To enhance further comprehension of the problem, this paper establishes a novel notation system and theoretical framework, successfully providing the switching manifold for high-order problems in the form of switching law. Through deriving properties of switching laws on signs and dimension, this paper proposes a definite condition for time-optimal control. Guided by the developed theory, a trajectory planning method named the manifold-intercept method (MIM) is developed. The proposed MIM can plan time-optimal jerk-limited trajectories with full state constraints, and can also plan near-optimal higher-order trajectories with negligible extra motion time. Numerical results indicate that the proposed MIM outperforms all baselines in computational time, computational accuracy, and trajectory quality by a large gap

Palatine tonsillar metastasis of lung adenocarcinoma: An unusual immunohistochemical phenotype and a potential diagnostic pitfall

Metastasis rarely occurs to the palatine tonsils. Herein, we present an exceedingly rare case of palatine tonsillar metastasis from poorly differentiated lung adenocarcinoma with anaplastic lymphoma kinase (ALK) mutation in a 51-year-old woman. The patient manifested clinically as pharyngalgia without obvious respiratory symptoms, with swelling tonsil histomorphologically resembling lymphoma and partially expressing the markers of epithelial and squamous cell carcinoma (CK5/6, P63, and P40). Due to the non-specific immunohistochemical expression, it is easily misdiagnosed as a primary poorly differentiated squamous cell carcinoma of the tonsil. This case highlights the importance of a comprehensive assessment of suspicious tonsillar lesions, that may be a sign of a primary malignancy elsewhere in the body

Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.Comment: AAAI 202

Defects in Human Methionine Synthase in cblG Patients

Inborn errors resulting in isolated functional methionine synthase deficiency fall into two complementation groups, cblG and cblE. Using biochemical approaches we demonstrate that one cblG patient has greatly reduced levels of methionine synthase while in another, the enzyme is specifically impaired in the reductive activation cycle. The biochemical data suggested that low levels of methionine synthase activity in the first patient may result from mutations in the catalytic domains of the enzyme, reduced transcription, or generation of unstable message or protein. Using Northern analysis, we demonstrate that the molecular basis for the biochemical phenotype in this patient is associated with greatly diminished steady-state levels of methionine synthase mRNA. The biochemical data on the second patient cell line implicated mutations specific to reductive activation, a function that is housed in the C-terminal AdoMet-binding domain and the intermediate B12-binding domain, in the highly homologous bacterial enzyme. We have detected two mutations in a compound heterozygous state, one that results in conversion of a conserved proline (1173) to a leucine residue and the other a deletion of an isoleucine residue (881). The crystal structure of the C-terminal domain of the Escherichia coli MS predicts that the Pro to Leu mutation could disrupt activation since it is embedded in a sequence that makes direct contacts with the bound AdoMet. Deletion of isoleucine in the B12-binding domain would result in shortening of a β-sheet. Our data provide the first evidence for mutations in the methionine synthase gene being culpable for the cblG phenotype. In addition, they suggest directly that mutations in methionine synthase can lead to elevated homocysteine, implicated both in neural tube defects and in cardiovascular disease