8,233 research outputs found

    Dual dynamic programming for stochastic programs over an infinite horizon

    Full text link
    We consider a dual dynamic programming algorithm for solving stochastic programs over an infinite horizon. We show non-asymptotic convergence results when using an explorative strategy, and we then enhance this result by reducing the dependence of the effective planning horizon from quadratic to linear. This improvement is achieved by combining the forward and backward phases from dual dynamic programming into a single iteration. We then apply our algorithms to a class of problems called hierarchical stationary stochastic programs, where the cost function is a stochastic multi-stage program. The hierarchical program can model problems with a hierarchy of decision-making, e.g., how long-term decisions influence day-to-day operations. We show that when the subproblems are solved inexactly via a dynamic stochastic approximation-type method, the resulting hierarchical dual dynamic programming can find approximately optimal solutions in finite time. Preliminary numerical results show the practical benefits of using the explorative strategy for solving the Brazilian hydro-thermal planning problem and economic dispatch, as well as the potential to exploit parallel computing.Comment: 45 pages. New experiments for hierarchical problem and writing update

    Life-Cycle Portfolio Choice with Stock Market Loss Framing: Explaining the Empirical Evidence

    Get PDF
    We develop a life-cycle model with optimal consumption, portfolio choice, and flexible work hours for households with loss-framing preferences giving them disutility if they experience losses from stock investments. Structural estimation using U.S. data shows that the model tracks the empirical age-pattern of stock market participants’ financial wealth, stock shares, and work hours remarkably well. Including stock market participation costs in the model allows us to also predict low stock market participations rates observed in the overall population. Allowing for heterogeneous agents further improves explanatory power and accounts for the observed discrepancy in wealth accumulation between stockholders and non-stockholders

    Interference-aware Demand-based User Scheduling in Precoded High Throughput Satellite Systems

    Get PDF
    In recent years, dynamic traffic demand requisites have driven the satellite communication service providers to implement reconfigurable demand-driven features to align the delivered throughput with the temporal and geographical variations of the traffic demand. Also, in current interference-limited High Throughput Satellite (HTS) systems, the resulting inter-beam co-channel interference can be mitigated by carefully performing precoding and user scheduling. Unfortunately, the conventional user scheduling algorithms fail to provide demand satisfaction for dynamic traffic demand requisites. Hence, in this paper, we focus on the user scheduling design for precoded satellite systems where both co-channel interference and user demands are taken into account. In particular, we first classify the sectors in each beam according to the interference they may cause to neighboring beams. Next, we formulate the scheduling problem such as the activation of neighboring beam sectors is avoided while proportionally dwelling on the sectors based on their traffic demands. The supporting numerical results for different demand distribution profiles validate the effectiveness of proposed interference-aware demand-based user scheduling over conventional scheduling techniques

    Image classification over unknown and anomalous domains

    Get PDF
    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Towards a non-equilibrium thermodynamic theory of ecosystem assembly and development

    Get PDF
    Non-equilibrium thermodynamics has had a significant historic influence on the development of theoretical ecology, even informing the very concept of an ecosystem. Much of this influence has manifested as proposed extremal principles. These principles hold that systems will tend to maximise certain thermodynamic quantities, subject to the other constraints they operate under. A particularly notable extremal principle is the maximum entropy production principle (MaxEPP); that systems maximise their rate of entropy production. However, these principles are not robustly based in physical theory, and suffer from treating complex ecosystems in an extremely coarse manner. To address this gap, this thesis derives a limited but physically justified extremal principle, as well as carrying out a detailed investigation of the impact of non-equilibrium thermodynamic constraints on the assembly of microbial communities. The extremal principle we obtain pertains to the switching between states in simple bistable systems, with switching paths that generate more entropy being favoured. Our detailed investigation into microbial communities involved developing a novel thermodynamic microbial community model, using which we found the rate of ecosystem development to be set by the availability of free-energy. Further investigation was carried out using this model, demonstrating the way that trade-offs emerging from fundamental thermodynamic constraints impact the dynamics of assembling microbial communities. Taken together our results demonstrate that theory can be developed from non-equilibrium thermodynamics, that is both ecologically relevant and physically well grounded. We find that broad extremal principles are unlikely to be obtained, absent significant advances in the field of stochastic thermodynamics, limiting their applicability to ecology. However, we find that detailed consideration of the non-equilibrium thermodynamic mechanisms that impact microbial communities can broaden our understanding of their assembly and functioning.Open Acces

    Full stack development toward a trapped ion logical qubit

    Get PDF
    Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates can be performed. The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator. This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated. The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger scale iterations.Open Acces

    Channel estimation and beam training with machine learning applications for millimetre-wave communication systems

    Get PDF
    The fifth generation (5G) wireless system will extend the capabilities of the fourth generation (4G) standards to serve more users and provide timely communication. To this end, the carriers of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave (mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher data rates. As a result, the deployment of 5G networks is required to accommodate more antennas and offer pervasive coverage with controlled power consumption. The complexity of 5G systems introduces new challenges to traditional signal processing techniques. To address these challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication systems. ML can learn patterns from datasets to achieve control and optimisation of complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel estimation methods and beam training strategies with the application of ML algorithms for mmWave wireless systems. Firstly, the channel estimation and signal detection problem is investigated for orthogonal frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep neural network (DNN)-based joint channel estimation and signal detection approach is proposed to achieve multi-user detection in a one-shot process for non-orthogonal multiple access (NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by learning the channel implicitly from suitable training. The proposed approach can be adapted to work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput (MIMO) systems. This DNN-based approach is shown to provide good performance for OFDM systems that suffer from severe inter-symbol interference or where small numbers of pilot symbols are used. Secondly, the beam training and tracking problem is studied for mmWave channels with receiver mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity beam training strategy is proposed for mobile mmWave channels, which searches a set of selected beams obtained based on the recent beam search results. By searching only the adjacent beams to the one recently used, the proposed beam training strategy can reduce the beam training delay significantly while maintaining high transmission rates. The proposed strategy works effectively for channel datasets generated using either the stochastic or the raytracing channel model. This strategy is shown to approach the performance for an exhaustive beam search while saving up to 92% on the required beam training overhead. Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm is proposed, which can intelligently switch between different beam training methods such that the average beam training overhead is minimised while achieving good spectral efficiency or energy efficiency performance. Given the desired performance requirement in the reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised for the current channel condition by controlling the number of activated RF chains. The DRL-based approach can adjust the amount of beam training overhead required according to the dynamics of the environment. This approach can provide a good overhead-performance trade-off and achieve higher data rates in channels with significant levels of signal blockage

    Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

    Get PDF
    This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordThe sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city
    • …
    corecore