304 research outputs found
Efficient Speech Translation with Pre-trained Models
When building state-of-the-art speech translation models, the need for large computational resources is a significant obstacle due to the large training data size and complex models. The availability of pre-trained models is a promising opportunity to build strong speech translation systems efficiently. In a first step, we investigate efficient strategies to build cascaded and end-to-end speech translation systems based on pre-trained models. Using this strategy, we can train and apply the models on a single GPU. While the end-to-end models show superior translation performance to cascaded ones, the application of this technology has a limitation on the need for additional end-to-end training data. In a second step, we proposed an additional similarity loss to encourage the model to generate similar hidden representations for speech and transcript. Using this technique, we can increase the data efficiency and improve the translation quality by 6 BLEU points in scenarios with limited end-to-end training data
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Optimal control is notoriously difficult for stochastic nonlinear systems.
Ren et al. introduced Spectral Dynamics Embedding for developing reinforcement
learning methods for controlling an unknown system. It uses an
infinite-dimensional feature to linearly represent the state-value function and
exploits finite-dimensional truncation approximation for practical
implementation. However, the finite-dimensional approximation properties in
control have not been investigated even when the model is known. In this paper,
we provide a tractable stochastic nonlinear control algorithm that exploits the
nonlinear dynamics upon the finite-dimensional feature approximation, Spectral
Dynamics Embedding Control (SDEC), with an in-depth theoretical analysis to
characterize the approximation error induced by the finite-dimension truncation
and statistical error induced by finite-sample approximation in both policy
evaluation and policy optimization. We also empirically test the algorithm and
compare the performance with Koopman-based methods and iLQR methods on the
pendulum swingup problem
Managing Inventory and Financing Decisions Under Ambiguity
Micro, small and medium-sized enterprises (MSMEs) face persistent challenges in raising capitals, and one of the practical reasons could be the high level of ambiguity in this sector. As many not-for-profit organizations or governmental agencies strengthen financial supports to MSMEs, the important issue of stimulating growth while protecting fund providers under ambiguity arises. We propose a robust optimization framework to jointly determine the firm's production planning and financing decisions in a principal-agent model with the presence of distributional ambiguity. We apply the notion of absolute robustness to derive a financing agreement that is both feasibility-robust and performance-robust. We assume that both the firm and the investor base their decisions on two fundamental descriptive statistics: the mean and the variance of the demand. The firm jointly determines the production quantity and financial agreement to maximize the worst-case expected profit, while the investor approves the financial agreement if the worst case expected return can cover the cost of capital. We show that equity financing is one of the robust optimal financing agreements. We also consider loan financing as an alternative. We derive the firm's robust optimal interest rate and production quantity in closed forms. Notably, the robust optimal interest rate depends on the demand variability and the asset recovery ratio, which comprehensively considers the value of collateral, initial capital, and production quantity
Near-Field Integrated Sensing, Positioning, and Communication: A Downlink and Uplink Framework
A near-field integrated sensing, positioning, and communication (ISPAC)
framework is proposed, where a base station (BS) simultaneously serves multiple
communication users and carries out target sensing and positioning. A novel
double-array structure is proposed to enable the near-field ISPAC at the BS.
Specifically, a small-scale assisting transceiver (AT) is attached to the
large-scale main transceiver (MT) to empower the communication system with the
ability of sensing and positioning. Based on the proposed framework, the joint
angle and distance Cram\'er-Rao bound (CRB) is first derived. Then, the CRB is
minimized subject to the minimum communication rate requirement in both
downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target
positioning algorithm is proposed and a penalty dual decomposition (PDD)-based
double-loop algorithm is developed to tackle the non-convex optimization
problem. 2) For uplink ISPAC, an uplink target positioning algorithm is
proposed and an efficient alternating optimization algorithm is conceived to
solve the non-convex CRB minimization problem with coupled user communication
and target probing design. Both proposed optimization algorithms can converge
to a stationary point of the CRB minimization problem. Numerical results show
that: 1) The proposed ISPAC system can locate the target in both angle and
distance domains merely relying on single BS and limited bandwidths; and 2) the
positioning performance achieved by the hybrid-analog-and-digital ISPAC
approaches that achieved by fully digital ISPAC when the communication rate
requirement is not stringent.Comment: 13 pages, 6 figure
Determinants of the RFMLR Circulant Matrices with Perrin, Padovan, Tribonacci, and the Generalized Lucas Numbers
The row first-minus-last right (RFMLR) circulant matrix and row last-minus-first left (RLMFL) circulant matrices are two special pattern matrices. By using the inverse factorization of polynomial, we give the exact formulae of determinants of the two pattern matrices involving Perrin, Padovan, Tribonacci, and the generalized Lucas sequences in terms of finite many terms of these sequences
Automatic shock detection, extraction and fitting in schlieren and shadowgraph visualization
No abstract available
A New Method to Solve Zero-Sum Games under Moment Conditions
When only the moments (mean, variance or t-th moment) of the underline distribution are known, numerous
max-min optimization models can be interpreted as a zero-sum game, in which the decision maker (DM)
chooses actions to maximize her expected profit while Adverse Nature chooses a distribution subject to
the moments conditions to minimize DM’s expected profit. We propose a new method to efficiently solve
this class of zero-sum games under moment conditions. By applying the min-max inequality, our method
reformulates the zero-sum game as a robust moral hazard model, in which Adverse Nature chooses both the
distribution and actions to minimize DM’s expected profit subject to incentive compatibility (IC) constraints.
Under quasi-concavity, these IC constraints are replaced by the first-order conditions, which give rise to
extra moment constraints. Interestingly, these extra moment constraints drastically reduce the number of corner points to be considered in the corresponding semi-infinite programming models. We show that in the equilibrium, these moment constraints are binding but have
zero Lagrangian multipliers and thus facilitate closed-form solutions in several application examples with
different levels of complexity. The high efficiency of the method enables us to solve a large class of zero-sum
games and the corresponding max-min robust optimization models
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