Stripe, checkerboard, and liquid-crystal ordering from anisotropic p-orbital Fermi surfaces in optical lattices

We study instabilities of single-species fermionic atoms in the p-orbital bands in two-dimensional optical lattices at noninteger filling against interactions. Charge density wave and orbital density wave orders with stripe or checkerboard patterns are found for attractive and repulsive interactions, respectively. The superfluid phase, usually expected of attractively interacting fermions, is strongly suppressed. We also use field theory to analyze the possible phase-transitions from orbital stripe order to liquid-crystal phases and obtain the phase diagram. The condition of nearly-perfect Fermisurface nesting, which is key to the above results, is shown robustly independent of fermion fillings in such p-orbital systems, and the $(2k_F,\pm2k_F)$ momentum of density wave oscillation is highly tunable. Such remarkable features show the promise of making those exotic orbital phases, which are of broad interest in condensed-matter physics, experimentally realizable with optical lattice gases.Comment: final version, 8 pages, 5 figure

Time reversal symmetry breaking of pp-orbital bosons in a one-dimensional optical lattice

We study bosons loaded in a one-dimensional optical lattice of two-fold $p$-orbital degeneracy at each site. Our numerical simulations find an anti-ferro-orbital p$_x$+ip$_y$, a homogeneous p$_x$ Mott insulator phase and two kinds of superfluid phases distinguished by the orbital order (anti-ferro-orbital and para-orbital). The anti-ferro-orbital order breaks time reversal symmetry. Experimentally observable evidence is predicted for the phase transition between the two different superfluid phases. We also discover that the quantum noise measurement is able to provide a concrete evidence of time reversal symmetry breaking in the first Mott phase.Comment: 4+ pages, version accepted by Phys. Rev. Let

A method for incremental discovery of financial event types based on anomaly detection

Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation of a small number of event types does not meet our research needs for more complex tasks such as the prediction of major financial events and the analysis of the ripple effects of financial events. In this paper, a three-stage approach is proposed to accomplish incremental discovery of event types. For an existing annotated financial event dataset, the three-stage approach consists of: for a set of financial event data with a mixture of original and unknown event types, a semi-supervised deep clustering model with anomaly detection is first applied to classify the data into normal and abnormal events, where abnormal events are events that do not belong to known types; then normal events are tagged with appropriate event types and abnormal events are reasonably clustered. Finally, a cluster keyword extraction method is used to recommend the type names of events for the new event clusters, thus incrementally discovering new event types. The proposed method is effective in the incremental discovery of new event types on real data sets.Comment: 11 pages,4 figure

Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering

Medical visual question answering (VQA) is a challenging task that requires answering clinical questions of a given medical image, by taking consider of both visual and language information. However, due to the small scale of training data for medical VQA, pre-training fine-tuning paradigms have been a commonly used solution to improve model generalization performance. In this paper, we present a novel self-supervised approach that learns unimodal and multimodal feature representations of input images and text using medical image caption datasets, by leveraging both unimodal and multimodal contrastive losses, along with masked language modeling and image text matching as pretraining objectives. The pre-trained model is then transferred to downstream medical VQA tasks. The proposed approach achieves state-of-the-art (SOTA) performance on three publicly available medical VQA datasets with significant accuracy improvements of 2.2%, 14.7%, and 1.7% respectively. Besides, we conduct a comprehensive analysis to validate the effectiveness of different components of the approach and study different pre-training settings. Our codes and models are available at https://github.com/pengfeiliHEU/MUMC.Comment: accepted by MICCAI202

Research on unsteady performance of a two-stage self-priming centrifugal pump

In order to study the unsteady performance of a two-stage self-priming centrifugal pump, the unsteady numerical calculation in a two-stage self-priming centrifugal pump was performed and energy characteristics experiments and self-priming experiments were carried out. The pressure pulsation and radial force in the pump were then analyzed. The results show that numerical calculation values are close to the experiment values. Head deviation of the pump is less than 3 %, and efficiency deviation of the pump is less than 2 percentage points. Compared with monitoring point P1, the pressure fluctuation coefficient of monitoring point P3 at the design flow rate is reduced by 61 %. Compared with monitoring point P8, the pressure fluctuation coefficient of monitoring point P5 is reduced by 70 %. The radial force on the radial guide-vane is obviously smaller than that on the volute. Under the same flow rate, radial force on the volute of second-stage pump is almost 20 times larger than that on the radial guide-van of first-stage pump

Toward Image-Guided Automated Suture Grasping Under Complex Environments: A Learning-Enabled and Optimization-Based Holistic Framework

To realize a higher-level autonomy of surgical knot tying in minimally invasive surgery (MIS), automated suture grasping, which bridges the suture stitching and looping procedures, is an important yet challenging task needs to be achieved. This paper presents a holistic framework with image-guided and automation techniques to robotize this operation even under complex environments. The whole task is initialized by suture segmentation, in which we propose a novel semi-supervised learning architecture featured with a suture-aware loss to pertinently learn its slender information using both annotated and unannotated data. With successful segmentation in stereo-camera, we develop a Sampling-based Sliding Pairing (SSP) algorithm to online optimize the suture's 3D shape. By jointly studying the robotic configuration and the suture's spatial characteristics, a target function is introduced to find the optimal grasping pose of the surgical tool with Remote Center of Motion (RCM) constraints. To compensate for inherent errors and practical uncertainties, a unified grasping strategy with a novel vision-based mechanism is introduced to autonomously accomplish this grasping task. Our framework is extensively evaluated from learning-based segmentation, 3D reconstruction, and image-guided grasping on the da Vinci Research Kit (dVRK) platform, where we achieve high performances and successful rates in perceptions and robotic manipulations. These results prove the feasibility of our approach in automating the suture grasping task, and this work fills the gap between automated surgical stitching and looping, stepping towards a higher-level of task autonomy in surgical knot tying

Evaluation of the Pacific oyster marine aquaculture suitability in Shandong, China based on GIS and remote sensing

The Pacific oyster (Crassostrea gigas) is a marine aquaculture species with rapid production growth in recent years. China accounts for nearly 90% of global production by 2021, especially in Shandong province. Evaluating suitability is crucial for ensuring the sustainable growth of Pacific oyster marine aquaculture and achieving a blue transition. This study developed a suitability evaluation model for Pacific oyster marine aquaculture using a Geographic Information System (GIS), Maximum Entropy (MaxEnt) model, remote sensing, and reanalysis data. A literature review and Analytic Hierarchy Process (AHP) were used to establish an evaluation model encompassing water quality, hydrology, climate and meteorology, and socioeconomic factors. The results showed that within a 20 km range of the Shandong coast, 49% of the area was highly suitable, 51% was moderately suitable, and the overall annual high score proportion (HSP) fluctuated around 50%, with higher suitability observed in the spring and autumn. The inner bays of the coastal areas (Laizhou, Rongcheng, Jimo) exhibited high suitability (HSP over 80%); in contrast, the offshore areas (Changdao, Rushan) farther from the coast had lower suitability and showed significant monthly variations. The result was consistent with the spatial distribution and temporal variation of Shandong’s existing Pacific oyster marine aquaculture areas. The study also found that El Niño significantly impacts Rongcheng, Rushan, and Jimo during summer. We predicted an overall increase of suitability in the Shandong offshore areas under future climate change scenarios, with a more significant increase of suitability in the north. El Niño-Southern Oscillation (ENSO) influenced the concentration of parameters such as chlorophyll-a (Chl-a) and total suspended sediment (TSS) in the coastal waters through its impact on precipitation (Pr), resulting in suitability fluctuations

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table