Parallel Longest Increasing Subsequence and van Emde Boas Trees

This paper studies parallel algorithms for the longest increasing subsequence (LIS) problem. Let $n$ be the input size and $k$ be the LIS length of the input. Sequentially, LIS is a simple problem that can be solved using dynamic programming (DP) in $O(n\log n)$ work. However, parallelizing LIS is a long-standing challenge. We are unaware of any parallel LIS algorithm that has optimal $O(n\log n)$ work and non-trivial parallelism (i.e., $\tilde{O}(k)$ or $o(n)$ span). This paper proposes a parallel LIS algorithm that costs $O(n\log k)$ work, $\tilde{O}(k)$ span, and $O(n)$ space, and is much simpler than the previous parallel LIS algorithms. We also generalize the algorithm to a weighted version of LIS, which maximizes the weighted sum for all objects in an increasing subsequence. To achieve a better work bound for the weighted LIS algorithm, we designed parallel algorithms for the van Emde Boas (vEB) tree, which has the same structure as the sequential vEB tree, and supports work-efficient parallel batch insertion, deletion, and range queries. We also implemented our parallel LIS algorithms. Our implementation is light-weighted, efficient, and scalable. On input size $10^9$, our LIS algorithm outperforms a highly-optimized sequential algorithm (with $O(n\log k)$ cost) on inputs with $k\le 3\times 10^5$. Our algorithm is also much faster than the best existing parallel implementation by Shen et al. (2022) on all input instances.Comment: to be published in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '23

4-Methyl-N-(9-methyl-9-aza­bicyclo­[3.3.1]non-3-yl)benzamide

The asymmetric unit of the title compound, C17H24N2O, contains three independent mol­ecules. In the crystal, mol­ecules are linked by weak N—H⋯O hydrogen bonds into chains parallel to the c axis

Impacts of the Local arm on the local circular velocity inferred from the Gaia DR3 young stars in the Milky Way

A simple one-dimensional axisymmetric disc model is applied to the kinematics of OB stars near the Sun obtained from Gaia DR3 catalogue. The model determines the 'local centrifugal speed' $V_\mathrm{c}(R_{0})$ - defined as the circular velocity in the Galactocentric rest frame, where the star would move in a near-circular orbit if the potential is axisymmetric with the local potential of the Galaxy. We find that the $V_\mathrm{c}(R_{0})$ values and their gradient vary across the selected region of stars within the solar neighbourhood. By comparing with an N-body/hydrodynamic simulation of a Milky Way-like galaxy, we find that the kinematics of the young stars in the solar neighbourhood is affected by the Local arm, which makes it difficult to measure $V_\mathrm{c}(R_{0})$. However, from the resemblance between the observational data and the simulation, we suggest that the known rotational velocity gap between the Coma Bernices and Hyades-Pleiades moving groups could be driven by the co-rotation resonance of the Local arm, which can be used to infer the azimuthally averaged circular velocity. We find that $V_\mathrm{c}(R)$ obtained from the $\mathrm{D}<2$ kpc sample is well matched with this gap at the position of the Local arm. Hence, we argue that our results from the $\mathrm{D}<2$ kpc sample, $V_\mathrm{c}(R_{0})= 233.95\pm2.24$ km $\mathrm{s}^{-1}$, is close to the azimuthally averaged circular velocity rather than the local centrifugal speed, which is influenced by the presence of the Local arm.Comment: 13 pages, 11 figure

Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters

In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT and GPT4 have demonstrated immense potential in constructing open-domain dialogue agents. However, aligning these agents with specific characters or individuals remains a considerable challenge due to the complexities of character representation and the lack of comprehensive annotations. In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment. The dataset encompasses all dialogue sessions (in both English and Chinese) from the Harry Potter series and is annotated with vital background information, including dialogue scenes, speakers, character relationships, and attributes. These extensive annotations may empower LLMs to unlock character-driven dialogue capabilities. Furthermore, it can serve as a universal benchmark for evaluating how well can a LLM aligning with a specific character. We benchmark LLMs on HPD using both fine-tuning and in-context learning settings. Evaluation results reveal that although there is substantial room for improvement in generating high-quality, character-aligned responses, the proposed dataset is valuable in guiding models toward responses that better align with the character of Harry Potter.Comment: 14 page

A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail

We present a novel dataset collected by ASOS (a major online fashion retailer) to address the challenge of predicting customer returns in a fashion retail ecosystem. With the release of this substantial dataset we hope to motivate further collaboration between research communities and the fashion industry. We first explore the structure of this dataset with a focus on the application of Graph Representation Learning in order to exploit the natural data structure and provide statistical insights into particular features within the data. In addition to this, we show examples of a return prediction classification task with a selection of baseline models (i.e. with no intermediate representation learning step) and a graph representation based model. We show that in a downstream return prediction classification task, an F1-score of 0.792 can be found using a Graph Neural Network (GNN), improving upon other models discussed in this work. Alongside this increased F1-score, we also present a lower cross-entropy loss by recasting the data into a graph structure, indicating more robust predictions from a GNN based solution. These results provide evidence that GNNs could provide more impactful and usable classifications than other baseline models on the presented dataset and with this motivation, we hope to encourage further research into graph-based approaches using the ASOS GraphReturns dataset.Comment: The ASOS GraphReturns dataset can be found at https://osf.io/c793h/. Accepted at FashionXRecSys 2022 workshop. Published Versio

Accelerated Transport through Sliding Dynamics of Rodlike Particles in Macromolecular Networks

Transport of rodlike particles in macromolecular networks is critical for many important biological processes and technological applications. Here, we report that speeding-up dynamics occurs once the rod length L reaches around integral multiple of the network mesh size ax. We find that such a fast diffusion follows the sliding dynamics and demonstrate it to be anomalous yet Brownian. The good agreement between theoretical analysis and simulations corroborates that sliding dynamics is an intermediate regime between hopping and Brownian dynamics, and suggests a mechanistic interpretation based on the rod-length dependent entropic free energy barrier. These theoretical findings are captured by the experimental observations of rods in synthetic networks, and bring new insight into the physics of the transport dynamics in confined media of networks

Tunable quantum dots in monolithic Fabry-Perot microcavities for high-performance single-photon sources

Cavity-enhanced single quantum dots (QDs) are the main approach towards ultra-high-performance solid-state quantum light sources for scalable photonic quantum technologies. Nevertheless, harnessing the Purcell effect requires precise spectral and spatial alignment of the QDs' emission with the cavity mode, which is challenging for most cavities. Here we have successfully integrated miniaturized Fabry-Perot microcavities with a piezoelectric actuator, and demonstrated a bright single photon source derived from a deterministically coupled QD within this microcavity. Leveraging the cavity-membrane structures, we have achieved large spectral-tunability via strain tuning. On resonance, we have obtained a high Purcell factor of approximately 9. The source delivers single photons with simultaneous high extraction efficiency of 0.58, high purity of 0.956(2) and high indistinguishability of 0.922(4). Together with a small footprint, our scheme facilitates the scalable integration of indistinguishable quantum light sources on-chip, and therefore removes a major barrier to the solid-state quantum information platforms based on QDs.Comment: 12 pages, 4 figure

Observation of hole injection boost via two parallel paths in Pentacene thin-film transistors by employing Pentacene: 4, 4"-tris(3-methylphenylphenylamino) triphenylamine: MoO₃ buffer layer

ABSTRACT: Pentacene organic thin-film transistors (OTFTs) were prepared by introducing 4, 4"-tris(3-methylphenylphenylamino) triphenylamine (m-MTDATA): MoO₃, Pentacene: MoO₃, and Pentacene: m-MTDATA: MoO₃ as buffer layers. These OTFTs all showed significant performance improvement comparing to the reference device. Significantly, we observe that the device employing Pentacene: m-MTDATA: MoO₃ buffer layer can both take advantage of charge transfer complexes formed in the m-MTDATA: MoO₃ device and suitable energy level alignment existed in the Pentacene: MoO₃ device. These two parallel paths led to a high mobility, low threshold voltage, and contact resistance of 0.72 cm(2)/Vs, -13.4 V, and 0.83 k Omega at V-ds = -100 V. This work enriches the understanding of MoO₃ doped organic materials for applications in OTFTs. (C) 2014 Author(s

Evaluation of a clinical pharmacist-led antimicrobial stewardship program in a neurosurgical intensive care unit: a pre-and post-intervention cohort study

Background: Antimicrobial resistance poses a significant challenge in neurosurgical intensive care units (ICU). The excessive use of broad-spectrum antibiotics is closely linked to the emergence and dissemination of drug-resistant bacteria within neurosurgical ICUs. This study assessed the effects of implementing a comprehensive Antimicrobial Stewardship (AMS) program in a neurosurgical ICU setting.Methods: From April 2022 to September 2022, an AMS program was implemented in the neurosurgical ICU. The program involved the regular presence of a pharmacist and an infectious disease physician who conducted prospective audits and provided feedback. To assess the impact of the AMS program, the outcome measures were compared between the AMS period and the 6 months before AMS implementation (pre-AMS period). The primary outcome was the use of antibacterial agents, including anti-pseudomonal beta-lactams (APBLs), polymyxin, and tigecycline. Additionally, the study evaluated the appropriateness of antimicrobial de-escalation and the susceptibility of Gram-negative bacilli to antimicrobial agents.Results: A total of 526 were included during the AMS period, while 487 patients were included in the pre-AMS period. The two groups had no significant differences in disease severity and mortality rates. During the AMS period, there was a notable decrease in the use of APBLs as empiric treatment (43.92% vs. 60.99%, p &lt; 0.001). Multi-drug resistant organism (MDRO) infections decrease significantly during AMS period (11.03% vs. 18.48%, p &lt; 0.001). The number of prescription adjustment increased significantly in all patients (0 item vs. 0 item, p &lt; 0.001) and MDRO-positive patients (3 items vs. 2 items, p &lt; 0.001) during the AMS period. Additionally, appropriate antimicrobial de-escalation for patients with MDRO showed improvement during the AMS period (39.66% vs. 20%, p = 0.001). Polymyxin utilization also decreased during the AMS period (15.52% vs. 31.11%, p = 0.034). Furthermore, the susceptibility of Gram-negative Bacilli isolates to APBLs was significantly higher during the AMS period.Conclusion: Implementing a comprehensive pharmacist-led AMS program led to a decrease in the use of antibacterial agents. This reduction in usage is significant because it can potentially delay the emergence of bacterial resistance