Online Nash Welfare Maximization Without Predictions

Nash welfare maximization is widely studied because it balances efficiency and fairness in resource allocation problems. Banerjee, Gkatzelis, Gorokh, and Jin (2022) recently introduced the model of online Nash welfare maximization with predictions for $T$ divisible items and $N$ agents with additive utilities. They gave online algorithms whose competitive ratios are logarithmic. We initiate the study of online Nash welfare maximization \emph{without predictions}, assuming either that the agents' utilities for receiving all items differ by a bounded ratio, or that their utilities for the Nash welfare maximizing allocation differ by a bounded ratio. We design online algorithms whose competitive ratios only depend on the logarithms of the aforementioned ratios of agents' utilities and the number of agents

Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing Systems (NeurIPS) 201

Supercontinuum generation and carrier envelope offset frequency measurement in a tapered single-mode fiber

We report supercontinuum generation by launching femtosecond Yb fiber laser pulses into a tapered single-mode fiber of 3 um core diameter. A spectrum of more than one octave, from 550 to 1400 nm, has been obtained with an output power of 1.3 W at a repetition rate of 250 MHz, corresponding to a coupling efficiency of up to 60%. By using a typical f-2f interferometer, the carrier envelope offset frequency was measured and found to have a signal-to-noise ratio of nearly 30 dB.Comment: 10 pages, accepted by Appl Phys

SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition

Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1

Perioperative Neurocognitive Disorder: State of the Preclinical Science.

The purpose of this article is to provide a succinct summary of the different experimental approaches that have been used in preclinical postoperative cognitive dysfunction research, and an overview of the knowledge that has accrued. This is not intended to be a comprehensive review, but rather is intended to highlight how the many different approaches have contributed to our understanding of postoperative cognitive dysfunction, and to identify knowledge gaps to be filled by further research. The authors have organized this report by the level of experimental and systems complexity, starting with molecular and cellular approaches, then moving to intact invertebrates and vertebrate animal models. In addition, the authors' goal is to improve the quality and consistency of postoperative cognitive dysfunction and perioperative neurocognitive disorder research by promoting optimal study design, enhanced transparency, and "best practices" in experimental design and reporting to increase the likelihood of corroborating results. Thus, the authors conclude with general guidelines for designing, conducting and reporting perioperative neurocognitive disorder rodent research

Sub-100 fs watt-level Kerr-lens mode-locked Yb:CaYAlO4 laser with a gigahertz repetition rate

We report a 1.04 GHz high-power Kerr-lens mode-locked Yb:CaYAlO4 laser pumped by a single-mode fiber laser at 976 nm. Based on a bow-tie cavity, stable unidirectional mode-locked operation is obtained with an output coupler of 1.6%. The oscillator delivers pulses with an average power of 1.46 W and with the pulse duration of 99 fs, which, to the best of our knowledge, is the first gigahertz-level Kerr-lens mode-locked laser based on the Yb:CaYAlO4 gain medium. We believe that the watt-level solid-state femtosecond laser at GHz would be an excellent source for developing time-resolved broadband dual-comb spectroscopy