8,233 research outputs found
Dual dynamic programming for stochastic programs over an infinite horizon
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
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
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Efficient Neural Network Verification Using Branch and Bound
Neural networks have demonstrated great success in modern machine learning systems. However, they remain susceptible to incorrect corner-case behaviors, often behaving unpredictably and producing surprisingly wrong results. Therefore, it is desirable to formally guarantee their trustworthiness for certain robustness properties when applied to safety-/security-sensitive systems like autonomous vehicles and aircraft. Unfortunately, the task is extremely challenging due to the complexity of neural networks, and traditional formal methods were not efficient enough to verify practical properties. Recently, a Branch and Bound (BaB) framework is generally extended for neural network verification and shows great success in accelerating the verification.
This dissertation focuses on state-of-the-art neural network verifiers using BaB. We will first introduce two efficient neural network verifiers ReluVal and Neurify using basic BaB approaches involving two main steps: (1) They will recursively split the original verification problem into easier independent subproblems by splitting input or hidden neurons; (2) For each split subproblem, we propose an efficient and tight bound propagation method called symbolic interval analysis, producing sound estimated bounds for outputs using convex linear relaxations. Both ReluVal and Neurify are three orders of magnitude faster than previously state-of-the-art formal analysis systems on standard verification benchmarks.
However, basic BaB approaches like Neurify have to construct each subproblem into a Linear Programming (LP) problem and solve it using expensive LP solvers, significantly limiting the overall efficiency. This is because each step of BaB will introduce neuron split constraints (e.g., a ReLU neuron larger or smaller than 0), which are hard to be handled by existing efficient bound propagation methods. We propose novel designs of bound propagation method -CROWN and its improved variance -CROWN, solving the verification problem by optimizing Lagrangian multipliers and with gradient ascent without requiring to call any expensive LP solvers. They were built based on previous work CROWN, a generalized efficient bound propagation method using linear relaxation. BaB verification using -CROWN and -CROWN cannot only provide tighter output estimations than most of the bound propagation methods but also can fully leverage the accelerations by GPUs with massive parallelization.
Combining our methods with BaB empowers the state-of-the-art verifier ,-CROWN (alpha-beta-CROWN), the winning tool in the second International Verification of Neural Networks Competition (VNN-COMP 2021) with the highest total score. Our $\alpha,-CROWN can be three orders of magnitude faster than LP solver based BaB verifiers and is notably faster than all existing approaches on GPUs. Recently, we further generalize -CROWN and propose an efficient iterative approach that can tighten all intermediate layer bounds under neuron split constraints and strengthen the bound tightness without LP solvers. This new approach in BaB can greatly improve the efficiency of ,-CROWN, especially on several challenging benchmarks.
Lastly, we study verifiable training that incorporates verification properties in training procedures to enhance the verifiable robustness of trained models and scale verification to larger models and datasets. We propose two general verifiable training frameworks: (1) MixTrain that can significantly improve verifiable training efficiency and scalability and (2) adaptive verifiable training that can improve trained verifiable robustness accounting for label similarity. The combination of verifiable training and BaB based verifiers opens promising directions for more efficient and scalable neural network verification
Interference-aware Demand-based User Scheduling in Precoded High Throughput Satellite Systems
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
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
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
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
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
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
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