Quantitative and modeling aspects of optimal decision making under uncertainty

This dissertation focuses on the problem of decision making under uncertainty, more precisely, the quantitative and modeling aspects of "how to acquire and, in turn, exploit information optimally for decision-making in stochastic environments". To address the challenges posed by different types of uncertainty, a range of methods have been developed in the fields of stochastic control under partial information, dynamic information acquisition, data-driven optimization, model uncertainty, and robust optimization. Specifically, this dissertation is composed by two parts: The first part focuses on an offline data-driven decision-making problem with side information. With abundant data routinely collected in many industries to support decision-making, historical data with numerous side information–temporal, spatial, social, or economical–are available prior to the decision making and reveals partial information on the randomness of the problem. The challenge for these high-dimensional problems is that the empirical distribution constructed from the observed data is not representative of the underlying true distribution between contextual information and decisions, and strategies solely based on the empirical data can lead to poor performance when implemented. Therefore, a fundamental problem in data-driven decision-making under uncertainty, as well as in statistical learning, is finding solutions that perform well not only on the observed data but also on new and previously unseen data. To hedge against the distributional uncertainty of the offline dataset, this dissertation provides an end-to-end learning framework, based on distributionally robust stochastic optimization (DRSO), that prescribes a non-parametric policy with certified robustness, provable optimality, and efficient implementation. Specifically, we study policy optimization for a series of feature-based decision-making problems, which seeks an end-to-end policy that renders an explicit mapping from features to decisions. In this dissertation, we first consider a Wasserstein robust optimization framework, where we highlight our contribution in finding an optimal robust policy without restricting onto a parametric family while still maintaining computational efficiency and interpretability. More specifically, by exploiting the structure of the optimal policy, we identify a new class of policies that are proven to be robust optimal and can be computed by linear programming. We apply our work in newsvendor problem. Furthermore, we propose a new uncertainty set based on causal transport distance which contains distributions that share a similar conditional information structure with the nominal distribution. We derive a tractable dual reformulation for evaluating the worst-case expected cost and show that the worst-case distribution has a similar conditional information structure as the nominal distribution. We identify tractable cases to find the optimal decision rules over an affine class or the entire nonparametric class, and apply our work in conditional regression, incumbent pricing and portfolio selection. The second part is concerned with dynamic information acquisition with sequential decision-making and differential information sources. When involving dynamic learning to facilitate decision making, since the decision makers often have imperfect and costly information, they encounter a trade-off between the information learning and the expected payoff, given the limited information. For example, when comparing new technologies, the firm often spends a considerable amount of funds and time on research and development (R&D) to identify the best technology to adopt. Other examples include investors designing algorithms to learn about the return of different assets, scientists conducting research to investigate the validity of different hypotheses, etc. From the viewpoint of dynamic information acquisition, the practically important features are the choice of "what to learn", as well as "when to learn and stop learning". Most of the decision-making problems considered in this line of work are static (i.e. a single, irreversible decision) problems which, however, over-simplify the structure of many real-world applications that require dynamic or sequential decisions. Moreover, the information acquisition source in these studies typically remains constant (e.g. a single noise signal) throughout the decision process, failing to capture the adaptive nature of decision makers in response to stochastically changing environments. Herein, we introduce a general framework in which we allow for both sequential (possibly reversible) decisions and dynamically changing information sources (distinct signals), and it also includes the cost of acquiring information across time. We analyze a benchmark example, motivated by the return/exchange policies in e-commerce platforms. Specifically, we introduce a sequential decision-making problem that allows decision makers to reverse their initial decisions and their costly information acquisition setting to change accordingly. We investigate the optimal strategies for information acquisition and decision reversal, and carry out a complete sensitivity and asymptotic analysis on how decision makers can effectively adapt their learning behavior to ultimately achieve the best decision-making outcomes. In what follows, we describe each approach separately. For each part, we introduce the corresponding model, construct solutions, and provide a detailed analytical methodology.Mathematic

ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.Comment: 11 pages, 9 figure

Full Quantum Dynamics of Complex Chemical System——Modelling Excited State Proton Transfer

Non-equilibrium dynamics of chemical and biological systems generally take place inchemical reactions and biological functions. Proton transfer is fundamental and ubiquitous whose mechanisms provide basic insights for more complex processes. Excited state proton transfer reaction (ESIPT) is the ideal model system for studying proton transfer mechanism because its dynamics can be controlled and tracked by light. As the general challenge faced in studying non-equilibrium proton transfer, complex quantum interactions among multiple degrees of freedom causes that the field does not have well-established theory at present. In this dissertation, we combine complex system methodology and theory of open quantum system to model ESIPT and provide mechanistic understanding. We construct an effective Hamiltonian of open quantum dynamics to simulate the quantum interplay of electron, proton, molecular skeleton, solvent, and light. It precisely quantifies the time-resolved spectroscopies of two single-site molecules, which reveals the deterministic motion and interaction could be protonic-electronic transition induced by proton-electron vibronic coupling, rather than semiclassical skeleton deformation assisting ballistic proton delivery as thought before. The vibronic coupling interaction can cause resonant electron-proton oscillation that determines the oscillatory pattern of time-resolved spectroscopy. The same model framework is applied to a double-site molecule, which suggests that the experimental symmetry-dependent isotope effect could be from quantum interference between two reaction channels. The next step will be prediction and validation to confirm these novel mechanisms and keep on developing ESIPT effective Hamiltonian which can be useful to the broader field of condensed phase chemical dynamics

Az "egy övezet, egy út" együttműködés Kína és Európa között

China has become the second largest economy in the world, and the European economy is also slowly recovering. The EU is China’s largest trading partner, China is the EU’s second largest trading partner and Europe’s largest source of imports. The "One Belt and One Road" cooperation between China and Europe has made the economic relationship between the two parties better and better.BSc/BABA in Business Administration and Managemen

A comprehensive ensemble model for comparing the allosteric effect of ordered and disordered proteins.

Intrinsically disordered proteins/regions (IDPs/IDRs) are prevalent in allosteric regulation. It was previously thought that intrinsic disorder is favorable for maximizing the allosteric coupling. Here, we propose a comprehensive ensemble model to compare the roles of both order-order transition and disorder-order transition in allosteric effect. It is revealed that the MWC pathway (order-order transition) has a higher probability than the EAM pathway (disorder-order transition) in allostery, suggesting a complicated role of IDPs/IDRs in regulatory proteins. In addition, an analytic formula for the maximal allosteric coupling response is obtained, which shows that too stable or too unstable state is unfavorable to endow allostery, and is thus helpful for rational design of allosteric drugs

A Simple Duality Proof for Wasserstein Distributionally Robust Optimization

We present a short and elementary proof of the duality for Wasserstein distributionally robust optimization, which holds for any arbitrary Kantorovich transport distance, any arbitrary measurable loss function, and any arbitrary nominal probability distribution, as long as certain interchangeability principle holds

Optimization Strategy for Line Loss Reduction of Distribution Network Based on Multi-distributed Photovoltaic Access

The number of distributed photovoltaic power generation systems connected to the system is increasing, especially for residents and non-residents. A large amount of photovoltaic grid-connected power brings new problems to the line loss management of the distribution network. This paper proposes a theoretical calculation model of line loss for distribution network with multi-distributed photovoltaic access. This paper adopts the actual case of Shanghai Power Grid, calculates based on the mathematical model proposed, and puts forward targeted optimization suggestions by comparing the simulation analysis results

Isolation and Characterization of a Newly Discovered Phage, V-YDF132, for Lysing Vibrio harveyi

A newly discovered lytic bacteriophage, V-YDF132, which efficiently infects the pathogenic strain of Vibrio harveyi, was isolated from aquaculture water collected in Yangjiang, China. Electron microscopy studies revealed that V-YDF132 belonged to the Siphoviridae family, with an icosahedral head and a long noncontractile tail. The phage has a latent period of 25 min and a burst size of 298 pfu/infected bacterium. V-YDF132 was stable from 37 to 50 °C. It has a wide range of stability (pH 5–11) and can resist adverse external environments. In addition, in vitro the phage V-YDF132 has a strong lytic effect on the host. Genome sequencing results revealed that V-YDF132 has a DNA genome of 84,375 bp with a GC content of 46.97%. In total, 115 putative open reading frames (ORFs) were predicted in the phage V-YDF132 genome. Meanwhile, the phage genome does not contain any known bacterial virulence genes or antimicrobial resistance genes. Comparison of the genomic features of the phage V-YDF132 and phylogenetic analysis revealed that V-YDF132 is a newly discovered Vibrio phage. Multiple genome comparisons and comparative genomics showed that V-YDF132 is in the same genus as Vibrio phages vB_VpS_PG28 (MT735630.2) and VH2_2019 (MN794238.1). Overall, the results indicate that V-YDF132 is potentially applicable for biological control of vibriosis

Investigation of New Accelerometer Based on Capacitive Micromachined Ultrasonic Transducer (CMUT) with Ring-Perforation Membrane

Capacitive micromachined ultrasonic transducer (CMUT) has been widely studied due to its excellent resonance characteristics and array integration. This paper presents the first study of the CMUT electrostatic stiffness resonant accelerometer. To improve the sensitivity of the CMUT accelerometer, this paper innovatively proposes the CMUT ring-perforation membrane structure, which effectively improves the acceleration sensitivity by reducing the mechanical stiffness of the elastic membrane. The acceleration sensitivity is 10.9 (Hz/g) in the acceleration range of 0–20 g, which is 100% higher than that of the conventional CMUT structure. This research contributes to the acceleration measurement field of CMUT and can effectively contribute to the breakthrough of vibration acceleration monitoring technology in aerospace, medical equipment, and automotive electronics