On improved deformable template matching for polygonal objects

In this paper, an improvement of deformable template matching algorithm for polygonal objects in grayscale images using two-dimensional deformable templates along orthogonal curves is presented. In the process of pre-computing extensions of the deformable template along orthogonal curves, the novel matching approach incorporates adapting knowledge-specific template discretization techniques appropriate for different polygonal objects and minimizing the improved internal and external energy terms containing inter-shape information of polygonal objects. In our application, this energy optimization problem of the deformable template is efficiently solved by a genetic algorithm (GA). Our algorithm has been successfully applied on synthetic images and real images. The experiment results show that the new approach provides more robust and accurate matching method.Facultad de Informátic

Aligning Linguistic Words and Visual Semantic Units for Image Captioning

Image captioning attempts to generate a sentence composed of several linguistic words, which are used to describe objects, attributes, and interactions in an image, denoted as visual semantic units in this paper. Based on this view, we propose to explicitly model the object interactions in semantics and geometry based on Graph Convolutional Networks (GCNs), and fully exploit the alignment between linguistic words and visual semantic units for image captioning. Particularly, we construct a semantic graph and a geometry graph, where each node corresponds to a visual semantic unit, i.e., an object, an attribute, or a semantic (geometrical) interaction between two objects. Accordingly, the semantic (geometrical) context-aware embeddings for each unit are obtained through the corresponding GCN learning processers. At each time step, a context gated attention module takes as inputs the embeddings of the visual semantic units and hierarchically align the current word with these units by first deciding which type of visual semantic unit (object, attribute, or interaction) the current word is about, and then finding the most correlated visual semantic units under this type. Extensive experiments are conducted on the challenging MS-COCO image captioning dataset, and superior results are reported when comparing to state-of-the-art approaches.Comment: 8 pages, 5 figures. Accepted by ACM MM 201

PACT/RAX Regulates the Migration of Cerebellar Granule Neurons in the Developing Cerebellum

PACT and its murine ortholog RAX were originally identified as a protein activator for the dsRNA-dependent, interferon-inducible protein kinase PKR. Recent studies indicated that RAX played a role in embryogenesis and neuronal development. In this study, we investigated the expression of RAX during the postnatal development of the mouse cerebellum and its role in the migration of cerebellar granule neurons (CGNs). High expression of RAX was observed in the cerebellum from postnatal day (PD) 4 to PD9, a period when the CGNs migrate from the external granule layer (EGL) to the internal granule layer (IGL). The migration of the EGL progenitor cells in vivo was inhibited by RAX knockdown on PD4. This finding was confirmed by in vitro studies showing that RAX knockdown impaired the migration of CGNs in cerebellar microexplants. PACT/RAX-regulated migration required its third motif and was independent of PKR. PACT/RAX interacted with focal adhesion kinase (FAK) and PACT/RAX knockdown disturbed the FAK phosphorylation in CGNs. These findings demonstrated a novel function of PACT/RAX in the regulation of neuronal migration

A Comparison of Ultrasound Guided Curettage With and Without Uterine Artery Embolization on Controlling Intraoperative Blood Loss for a Cesarean Scar Pregnancy Treatment: Study Protocol for a Randomized Clinical Trial

IntroductionCesarean scar pregnancy affects 6% of all ectopic pregnancies in women with prior cesarean section, and there is currently no consensus on the optimal treatment. Options of surgical treatment have a risk of intraoperative blood loss; therefore, uterine artery embolization (UAE) has been considered as an option of reducing intraoperative blood loss. However, UAE may be overused in clinical practice, especially in China. We present this protocol for a randomized clinical trial investigating the necessity of performing UAE for cesarean scar pregnancy, in combination with surgical suction curettage, taking into account the different subtypes of cesarean scar pregnancy. We recently developed a risk-scoring system (QRS) to estimate intraoperative blood loss, with 93.8% sensitivity and 6.3% false negative. Through this randomized clinical trial, we will retrospectively validate the QRS score on predicting intraoperative blood loss.Methods and AnalysisWe propose undertaking a randomized clinical trial sequentially recruiting 200 patients. All the patients will randomly receive ultrasound guided curettage with or without UAE. Data on the subtypes of cesarean scar pregnancy (Types 1 and II and III) detected by ultrasound will be collected before operation. The score on estimating intraoperative blood loss assessed by our recently developed quantitative risk-scoring system (QRS) will be collected before the operation. We will primarily compare the duration of the operation, intraoperative blood loss, and complications between the two groups. We will also retrospectively analyze the association of subtypes of cesarean scar pregnancy and the options of treatment and validate the QRS score. Outcomes of subsequent pregnancy within the 2-year follow-up will be secondary outcomes.Trial Registration Number[website], identifier ChiCTR2100041654

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

We investigate various prompting strategies for enhancing personalized recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs

DOTA: A Dynamically-Operated Photonic Tensor Core for Energy-Efficient Transformer Accelerator

The wide adoption and significant computing resource consumption of attention-based Transformers, e.g., Vision Transformer and large language models, have driven the demands for efficient hardware accelerators. While electronic accelerators have been commonly used, there is a growing interest in exploring photonics as an alternative technology due to its high energy efficiency and ultra-fast processing speed. Optical neural networks (ONNs) have demonstrated promising results for convolutional neural network (CNN) workloads that only require weight-static linear operations. However, they fail to efficiently support Transformer architectures with attention operations due to the lack of ability to process dynamic full-range tensor multiplication. In this work, we propose a customized high-performance and energy-efficient photonic Transformer accelerator, DOTA. To overcome the fundamental limitation of existing ONNs, we introduce a novel photonic tensor core, consisting of a crossbar array of interference-based optical vector dot-product engines, that supports highly-parallel, dynamic, and full-range matrix-matrix multiplication. Our comprehensive evaluation demonstrates that DOTA achieves a >4x energy and a >10x latency reduction compared to prior photonic accelerators, and delivers over 20x energy reduction and 2 to 3 orders of magnitude lower latency compared to the electronic Transformer accelerator. Our work highlights the immense potential of photonic computing for efficient hardware accelerators, particularly for advanced machine learning workloads.Comment: The short version is accepted by Next-Gen AI System Workshop at MLSys 202

ISPRS International Journal of Geo-Information / Extraction of terraces on the loess plateau from high-resolution DEMs and imagery utilizing object-based image analysis

Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.(VLID)219512

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website https://kddcup23.github.io/.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmarks; Dataset for KDD Cup 2023, https://kddcup23.github.io