Searcharxiv⌕ Search

arXiv subjects

Di Wu

Publications and source records attributed to Di Wu.

At least 451 records · Page 25Linked to original sources
arXiv2023

Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls

This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point) between any two successive layers, the first stage of the proposed method designs a layer-to-layer prediction model to estimate the temperature curves of the yet-to-print layer from measured temperatures of certain points on the previously printed layer. With measured/predicted temperature profiles of several points on the same layer, the second stage proposes a reduced order model (ROM) (intra-layer prediction model) to decompose and construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer. The training of ROM is performed with an extreme learning machine (ELM) for computational efficiency. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The test results indicate that the proposed prediction method could construct the thermal history of a yet-to-print layer within 0.1 seconds on a low-cost desktop computer. Meanwhile, the method has acceptable generalization capability in most cases from lower layers to higher layers in the same simulation, as well as from one simulation to a new simulation on different AM process parameters. More importantly, after fine-tuning the proposed method with limited experimental data, the relative errors of all predicted temperature profiles on a new experiment are smaller than 0.09, which demonstrates the applicability and generalization of the proposed two-stage thermal history prediction method in online applications for metal AM.

cs.LG↗
arXiv2023

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021.

cs.LG↗
arXiv2023

CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimental conditions and the lack of a standardized experimental design. In addition, the performance of data-driven approaches is often compared based on empirical analysis. The lack of reproducibility and availability of standardized evaluation tools (e.g., datasets, codebases) hinder the development and progress of data-driven methods for channel estimation and wireless communication in general. In this work, we introduce an initiative to build benchmarks that unify several data-driven OFDM channel estimation approaches. Specifically, we present CeBed (a testbed for channel estimation) including different datasets covering various systems models and propagation conditions along with the implementation of ten deep and traditional baselines. This benchmark considers different practical aspects such as the robustness of the data-driven models, the number and the arrangement of pilots, and the number of receive antennas. This work offers a comprehensive and unified framework to help researchers evaluate and design data-driven channel estimation algorithms.

cs.AI↗
arXiv2023

Insight-HXMT on-orbit thermal control status and thermal deformation impact analysis

Purpose: The Hard X-ray Modulation Telescope is China's first X-ray astronomy satellite launched on June 15th, 2017, dubbed Insight-HXMT. Active and passive thermal control measures are employed to keep devices at suitable temperatures. In this paper, we analyzed the on-orbit thermal monitoring data of the first 5 years and investigated the effect of thermal deformation on the point spread function (PSF) of the telescopes. Methods: We examined the data of the on-orbit temperatures measured using 157 thermistors placed on the collimators, detectors and their support structures and compared the results with the thermal control requirements. The thermal deformation was evaluated by the relative orientation of the two star sensors installed on the main support structure. its effect was estimated with evolution of the PSF obtained with calibration scanning observations of the Crab nebula. Conclusion: The on-orbit temperatures met the thermal control requirements thus far, and the effect of thermal deformation on the PSF was negligible after the on-orbit pointing calibration.

astro-ph.IM↗
arXiv2023

New constraints on primordial non-Gaussianity from missing two-loop contributions of scalar induced gravitational waves

We analyze the energy density spectrum of \acp{SIGW} using the NANOGrav 15-year data set, thereby constraining the primordial non-Gaussian parameter $f_{\mathrm{NL}}$. For the first time, we calculate the seventeen missing two-loop diagrams proportional to $f_{\mathrm{NL}}A_ζ^3$ that correspond to the two-point correlation function $\langle h^{λ,(3)}_{\mathbf{k}} h^{λ',(2)}_{\mathbf{k}'} \rangle$ for local-type primordial non-Gaussianity. The total energy density spectrum of \acp{SIGW} can be significantly suppressed by these two-loop diagrams. If \acp{SIGW} dominate the \acp{SGWB} observed in \ac{PTA} experiments, the parameter interval $f_{\mathrm{NL}}\in [-5,-1]$ is notably excluded based on NANOGrav 15-year data set. After taking into account abundance of \acp{PBH} and the convergence of the cosmological perturbation expansion, we find that the only possible parameter range for $f_{\mathrm{NL}}$ might be $-1\le f_{\mathrm{NL}}< 0$.

astro-ph.CO↗
arXiv2023

Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems

Large-scale battery energy storage systems (BESS) are helping transition the world towards sustainability with their broad use, among others, in electrified transportation, power grid, and renewables. However, optimal power management for them is often computationally formidable. To overcome this challenge, we develop a scalable approach in the paper. The proposed approach partitions the constituting cells of a large-scale BESS into clusters based on their state-of-charge (SoC), temperature, and internal resistance. Each cluster is characterized by a representative model that approximately captures its collective SoC and temperature dynamics, as well as its overall power losses in charging/discharging. Based on the clusters, we then formulate a problem of receding-horizon optimal power control to minimize the power losses while promoting SoC and temperature balancing. The cluster-based power optimization will decide the power quota for each cluster, and then every cluster will split the quota among the constituent cells. Since the number of clusters is much fewer than the number of cells, the proposed approach significantly reduces the computational costs, allowing optimal power management to scale up to large-scale BESS. Extensive simulations are performed to evaluate the proposed approach. The obtained results highlight a significant computational overhead reduction by more than 60% for a small-scale and 98% for a large-scale BESS compared to the conventional cell-level optimization. Experimental validation based on a 20-cell prototype further demonstrates its effectiveness and utility.

eess.SY↗
arXiv2023

Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks

Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models (LLMs) on a massive amount of diverse tasks with instructions. However, how to select new tasks to improve the performance and generalizability of IT models remains an open question. Training on all existing tasks is impractical due to prohibiting computation requirements, and randomly selecting tasks can lead to suboptimal performance. In this work, we propose active instruction tuning based on prompt uncertainty, a novel framework to identify informative tasks, and then actively tune the models on the selected tasks. We represent the informativeness of new tasks with the disagreement of the current model outputs over perturbed prompts. Our experiments on NIV2 and Self-Instruct datasets demonstrate that our method consistently outperforms other baseline strategies for task selection, achieving better out-of-distribution generalization with fewer training tasks. Additionally, we introduce a task map that categorizes and diagnoses tasks based on prompt uncertainty and prediction probability. We discover that training on ambiguous (prompt-uncertain) tasks improves generalization while training on difficult (prompt-certain and low-probability) tasks offers no benefit, underscoring the importance of task selection for instruction tuning.

cs.CL↗
arXiv2023

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.

cs.NI↗
arXiv2023

Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models

Keyphrase Generation (KPG) is a longstanding task in NLP with widespread applications. The advent of sequence-to-sequence (seq2seq) pre-trained language models (PLMs) has ushered in a transformative era for KPG, yielding promising performance improvements. However, many design decisions remain unexplored and are often made arbitrarily. This paper undertakes a systematic analysis of the influence of model selection and decoding strategies on PLM-based KPG. We begin by elucidating why seq2seq PLMs are apt for KPG, anchored by an attention-driven hypothesis. We then establish that conventional wisdom for selecting seq2seq PLMs lacks depth: (1) merely increasing model size or performing task-specific adaptation is not parameter-efficient; (2) although combining in-domain pre-training with task adaptation benefits KPG, it does partially hinder generalization. Regarding decoding, we demonstrate that while greedy search achieves strong F1 scores, it lags in recall compared with sampling-based methods. Based on these insights, we propose DeSel, a likelihood-based decode-select algorithm for seq2seq PLMs. DeSel improves greedy search by an average of 4.7% semantic F1 across five datasets. Our collective findings pave the way for deeper future investigations into PLM-based KPG.

cs.CL↗
arXiv2023

Topological classes of thermodynamics of the four-dimensional static accelerating black holes

In this paper, utilizing the generalized off shell Helmholtz free energy, we explore the topological numbers of the four-dimensional static accelerating black hole and its AdS extension, as well as the static charged accelerating black hole and its AdS extension. Our analysis reveals a profound and significant impact of the acceleration parameter on the topological numbers associated with the static black holes; and different values (nonzero) of the acceleration parameter do not affect the topological numbers of the corresponding four-dimensional static accelerating black holes. In addition, we demonstrate that the electric charge parameter has an important effect on the topological number of the static neutral accelerating black holes, and the cosmological constant has a remarkable influence on the topological number of the static accelerating black hole. Furthermore, it is interesting to observe that the difference between the topological number of the asymptotically flat static accelerating black hole and that of its corresponding asymptotically flat static nonaccelerating black hole is always unity, and the difference between the topological number of the asymptotically AdS static accelerating black hole and that of its corresponding asymptotically AdS static nonaccelerating black hole is always $-1$. This new observation leads us to conjure that it might be valid also for other accelerating black holes. Of course, this captivating conjecture requires empirical verification through comprehensive investigation into the topological numbers of other accelerating black holes and their corresponding usual counterparts.

hep-th↗
arXiv2023

Enhancing Building Energy Efficiency through Advanced Sizing and Dispatch Methods for Energy Storage

Energy storage and electrification of buildings hold great potential for future decarbonized energy systems. However, there are several technical and economic barriers that prevent large-scale adoption and integration of energy storage in buildings. These barriers include integration with building control systems, high capital costs, and the necessity to identify and quantify value streams for different stakeholders. To overcome these obstacles, it is crucial to develop advanced sizing and dispatch methods to assist planning and operational decision-making for integrating energy storage in buildings. This work develops a simple and flexible optimal sizing and dispatch framework for thermal energy storage (TES) and battery energy storage (BES) systems in large-scale office buildings. The optimal sizes of TES, BES, as well as other building assets are determined in a joint manner instead of sequentially to avoid sub-optimal solutions. The solution is determined considering both capital costs in optimal sizing and operational benefits in optimal dispatch. With the optimally sized systems, we implemented real-time operation using the model-based control (MPC), facilitating the effective and efficient management of energy resources. Comprehensive assessments are performed using simulation studies to quantify potential energy and economic benefits by different utility tariffs and climate locations, to improve our understanding of the techno-economic performance of different TES and BES systems, and to identify barriers to adopting energy storage for buildings. Finally, the proposed framework will provide guidance to a broad range of stakeholders to properly design energy storage in buildings and maximize potential benefits, thereby advancing affordable building energy storage deployment and helping accelerate the transition towards a cleaner and more equitable energy economy.

eess.SY↗
arXiv2023

Decoding Modular Reconfigurable Robots: A Survey on Mechanisms and Design

The intrinsic modularity and reconfigurability of modular reconfigurable robots (MRR) confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple MRR categories, each described by its respective morphology or capability characteristics, leading to some ambiguity in the taxonomy. This paper conducts a comprehensive survey encompassing the entirety of MRR hardware and design, spanning from the inception in 1985 to 2023. This paper introduces an innovative, unified conceptual framework for understanding MRR hardware, which encompasses three pivotal elements: connectors, actuators, and homogeneity. Through the utilization of this trilateral framework, this paper provide an intuitive understanding of the diverse spectrum of MRR hardware iterations while systematically deciphering and classifying the entire range, offering a more structured perspective. This survey elucidates the fundamental attributes characterizing MRRs and their compositional aspects, providinig insights into their design, technology, functionality, and categorization. Augmented by the proposed trilateral framework, this paper also elaborates on the trajectory of evolution, prevailing trends, principal challenges, and potential prospects within the field of MRRs.

cs.RO↗
arXiv2023

UvA-MT's Participation in the WMT23 General Translation Shared Task

This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English <-> Hebrew. In this competition, we show that by using one model to handle bidirectional tasks, as a minimal setting of Multilingual Machine Translation (MMT), it is possible to achieve comparable results with that of traditional bilingual translation for both directions. By including effective strategies, like back-translation, re-parameterized embedding table, and task-oriented fine-tuning, we obtained competitive final results in the automatic evaluation for both English -> Hebrew and Hebrew -> English directions.

cs.CL↗
arXiv2023

Revisiting mass formulas of the four-dimensional Reissner-Nordström-NUT-AdS solutions in a different metric form

Recently, the so-called ``consistent thermodynamics'' of the Lorentzian Reissner-Nordström (RN)-NUT-AdS$_4$ spacetimes has been pursued by a lot of efforts via different means. Among these attempts, we had proposed a novel idea that ``The NUT charge is a thermodynamical multihair" to successfully tackle with the subject. In this paper, we will adopt this strategy to reconsider the mass formulas of the RN-NUT-AdS$_4$ solutions but written in an alternative form, which had not been studied before in any existing literature and might be a most appropriate ansatz for the higher dimensional multiply NUTty-charged AdS spacetimes without any constraint condition. Here, we shall discuss the Christodoulou-Ruffini-like squared mass formula and the first law as well as the Bekenstein-Smarr mass formula by introducing the secondary hair $J_n = Mn$. For the sake of generality, we have introduced a dimensionless constant $w$ into the constant factor $Ξ$ in the solution expression so that when $w = 1$, all obtained results can reproduce those delivered in our previous work.

gr-qc↗
arXiv2023

Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing

3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersive and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.

cs.NI↗
arXiv2023

A Read Margin Enhancement Circuit with Dynamic Bias Optimization for MRAM

This brief introduces a read bias circuit to improve readout yield of magnetic random access memories (MRAMs). A dynamic bias optimization (DBO) circuit is proposed to enable the real-time tracking of the optimal read voltage across processvoltage-temperature (PVT) variations within an MRAM array. It optimizes read performance by adjusting the read bias voltage dynamically for maximum sensing margin. Simulation results on a 28-nm 1Mb MRAM macro show that the tracking accuracy of the proposed DBO circuit remains above 90% even when the optimal sensing voltage varies up to 50%. Such dynamic tracking strategy further results in up to two orders of magnitude reduction in the bit error rate with respect to different variations, highlighting its effectiveness in enhancing MRAM performance and reliability.

eess.SP↗
arXiv2023

Consistent mass formulas for higher even-dimensional Taub-NUT spacetimes and their AdS counterparts

Currently, there is a great deal of interest in the seeking of consistent thermodynamics of the Lorentzian Taub-NUT spacetimes. Despite a lot of ``satisfactory'' efforts have been made, all of these activities have been restricted to the four-dimensional cases, with the higher even-dimensional cases remaining unexplored. The aim of this article is to fill the gap, for the first time. To the end of this subject, we first adopt our own idea that ``The NUT charge is a thermodynamical multi-hair" to investigate the consistent thermodynamics of $D = 6, 8, 10$ Lorentzian Taub-NUT spacetimes without a cosmological constant. Similarly to the $D = 4$ cases as did in our previous works, we find that the first law and Bekenstein-Smarr mass formulas are perfectly satisfied if we still assign the secondary hair: $J_n = Mn$ as a conserved charge in both mass formulae. Turning to the cases with a nonzero cosmological constant, our treatment continues to work very well and all the results can be fairly generalized to the corresponding Taub-NUT AdS spacetimes in higher even-dimensions, although we do not know how to define and introduce a similar higher-dimensional version of the dual (magnetic) mass that is well known in four dimensions. Based upon the preceding results, we will also derive the reduced version of the mass formulae when the secondary hair $J_n$ is viewed as a redundant thermodynamic variable.

hep-th↗
arXiv2023

Consistent mass formulas for higher even-dimensional Reissner-Nordström-NUT-AdS spacetimes

In our previous work [D. Wu and S.-Q. Wu, Phys. Rev. D 108, 064034 (2023)], we have formulated consistent thermodynamic first law and Bekenstein-Smarr mass formulae for all higher even-dimensional uncharged Taub-NUT (AdS) spacetimes through the inclusion of a secondary hair $J_n = Mn$ as did in the four-dimensional NUT-charged cases [S.-Q. Wu and D. Wu, Phys. Rev. D 100, 101501(R) (2019)]. In this article, we will show that the corresponding Reissner-Nordström(RN)-NUT (AdS) cases in all higher even-dimensions can be coherently treated just like the uncharged cases if another secondary hair $Q_n \propto qn$ is further introduced as did in the four-dimensional dyonic NUT-charged case [D. Wu and S.-Q. Wu, Phys. Rev. D 105, 124013 (2022)]. As a by-product of our consequence, it is also demonstrated that there is no need to introduce the secondary hair $Q_n$ in the mass formulae for the four-dimensional purely electric-charged RN-NUT (AdS) spacetimes.

gr-qc↗