Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this paper outline a capacity planning problem in which a risk-averse firm reserves capacities with potential suppliers that are located in multiple low-cost countries. While demand is uncertain, the firm also faces multi-country foreign currency exposures. This study develops a mean-variance model that maximizes the firm’s optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk- averse firm, by using financial hedging, will achieve the same optimal utility as a risk-neutral firm. In this paper as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk-averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this paper concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility

Robot Learning from Heterogeneous Demonstration

Learning from Demonstration (LfD) has become a ubiquitous and user-friendly technique to teach a robot how to perform a task (e.g., playing Ping Pong) without the need to use a traditional programming language (e.g., C++). As these systems are increasingly being placed in the hands of everyday users, researchers are faced with the reality that end-users are a heterogeneous population with varying levels of skills and experiences. This heterogeneity violates almost universal assumptions in LfD algorithms that demonstrations given by users are near-optimal and uniform in how the task is accomplished. In this thesis, I present algorithms to tackle two specific types of heterogeneity: heterogeneous strategy and heterogeneous performance. First, I present Multi-Strategy Reward Distillation (MSRD), which tackles the problem of learning from users who have adopted heterogeneous strategies. MSRD extracts separate task reward and strategy reward, which represents task specification and demonstrator's strategic preference, respectively. We are able to extract the task reward that has 0.998 and 0.943 correlation with ground-truth reward on two simulated robotic tasks and successfully deploy it on a real-robot table-tennis task. Second, I develop two algorithms to address the problem of learning from suboptimal demonstration: SSRR and OP-AIRL. SSRR is a novel mechanism to regress over noisy demonstrations to infer an idealized reward function. OP-AIRL is a mechanism to learn a policy that more effectively teases out ambiguity from sub-optimal demonstrations. By combining SSRR with OP-AIRL, we are able to achieve a 688% and a 254% improvement over state-of-the-art on two simulated robot tasks.M.S

Solution-processed small-molecule solar cells: breaking the 10% power conversion efficiency.

A two-dimensional conjugated small molecule (SMPV1) was designed and synthesized for high performance solution-processed organic solar cells. This study explores the photovoltaic properties of this molecule as a donor, with a fullerene derivative as an acceptor, using solution processing in single junction and double junction tandem solar cells. The single junction solar cells based on SMPV1 exhibited a certified power conversion efficiency of 8.02% under AM 1.5 G irradiation (100 mW cm(-2)). A homo-tandem solar cell based on SMPV1 was constructed with a novel interlayer (or tunnel junction) consisting of bilayer conjugated polyelectrolyte, demonstrating an unprecedented PCE of 10.1%. These results strongly suggest solution-processed small molecular materials are excellent candidates for organic solar cells

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deployment in ubiquitous robotics applications. In this paper, we propose a novel LfD framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our approach (1) leverages learned strategies to construct policy mixtures for fast adaptation to new demonstrations, allowing for quick end-user personalization, (2) distills common knowledge across demonstrations, achieving accurate task inference; and (3) expands its model only when needed in lifelong deployments, maintaining a concise set of prototypical strategies that can approximate all behaviors via policy mixtures. We empirically validate that FLAIR achieves adaptability (i.e., the robot adapts to heterogeneous, user-specific task preferences), efficiency (i.e., the robot achieves sample-efficient adaptation), and scalability (i.e., the model grows sublinearly with the number of demonstrations while maintaining high performance). FLAIR surpasses benchmarks across three control tasks with an average 57% improvement in policy returns and an average 78% fewer episodes required for demonstration modeling using policy mixtures. Finally, we demonstrate the success of FLAIR in a table tennis task and find users rate FLAIR as having higher task (p<.05) and personalization (p<.05) performance

A polymer tandem solar cell with 10.6% power conversion efficiency.

An effective way to improve polymer solar cell efficiency is to use a tandem structure, as a broader part of the spectrum of solar radiation is used and the thermalization loss of photon energy is minimized. In the past, the lack of high-performance low-bandgap polymers was the major limiting factor for achieving high-performance tandem solar cell. Here we report the development of a high-performance low bandgap polymer (bandgap &lt;1.4 eV), poly[2,7-(5,5-bis-(3,7-dimethyloctyl)-5H-dithieno[3,2-b:2',3'-d]pyran)-alt-4,7-(5,6-difluoro-2,1,3-benzothia diazole)] with a bandgap of 1.38 eV, high mobility, deep highest occupied molecular orbital. As a result, a single-junction device shows high external quantum efficiency of &gt;60% and spectral response that extends to 900 nm, with a power conversion efficiency of 7.9%. The polymer enables a solution processed tandem solar cell with certified 10.6% power conversion efficiency under standard reporting conditions (25 °C, 1,000 Wm(-2), IEC 60904-3 global), which is the first certified polymer solar cell efficiency over 10%

LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM

LiDAR odometry is one of the essential parts of LiDAR simultaneous localization and mapping (SLAM). However, existing LiDAR odometry tends to match a new scan simply iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (BA) cannot be directly introduced into real-time odometry due to the intensive computation of large-scale global landmarks. Therefore, this letter designs a new strategy named a landmark map for bundle adjustment odometry (LMBAO) in LiDAR SLAM to solve these problems. First, BA-based odometry is further developed with an active landmark maintenance strategy for a more accurate local registration and avoiding cumulative errors. Specifically, this paper keeps entire stable landmarks on the map instead of just their feature points in the sliding window and deletes the landmarks according to their active grade. Next, the sliding window length is reduced, and marginalization is performed to retain the scans outside the window but corresponding to active landmarks on the map, greatly simplifying the computation and improving the real-time properties. In addition, experiments on three challenging datasets show that our algorithm achieves real-time performance in outdoor driving and outperforms state-of-the-art LiDAR SLAM algorithms, including Lego-LOAM and VLOM.Comment: 9 pages, 3 tables, 6 figure