Persistence approximation property for LpL^p operator algebras

In this paper, we define quantitative assembly maps for $L^p$ operator algebras when $p\in [1,\infty)$. Moreover, we study the persistence approximation property for quantitative $K$-theory of filtered $L^p$ operator algebras. Finally, in the case of crossed product $L^{p}$ operator algebras, we find a sufficient condition for the persistence approximation property. This allows to give some applications involving the $L^{p}$ coarse Baum-Connes conjecture.Comment: 32 page

Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval

The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits the algorithmic backend from deep learning, a handful of attacks are recently proposed to disrupt normal image retrieval. Unfortunately, the defense strategies in softmax classification are not readily available to be applied in the image retrieval domain. In this paper, we propose an efficient and unsupervised scheme to identify unique adversarial behaviors in the hamming space. In particular, we design three criteria from the perspectives of hamming distance, quantization loss and denoising to defend against both untargeted and targeted attacks, which collectively limit the adversarial space. The extensive experiments on four datasets demonstrate 2-23% improvements of detection rates with minimum computational overhead for real-time image queries

Product-based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201

Energy Efficient Channel Access Mechanism for IEEE 802.11ah based Networks

PhDIEEE 802.11ah is designed to support battery powered devices that are required to serve for several years in the Internet of Things networks. The Restricted Access Window (RAW) has been introduced in IEEE 802.11ah to address the scalability of thousands of densely deployed devices. As the RAW sizes entail the consumed energy to support the transmitting devices in the network, hence the control mechanism for RAW should be carefully devised for improving the overall energy e ciency of IEEE 802.11ah. This thesis presents a two-stage adaptive RAW scheme for IEEE 802.11ah to optimise the energy efficiency of massive channel access and transmission in the uplink communications for highly dense networks. The proposed scheme adaptively controls the RAW sizes and device transmission access by taking into account the number of devices per RAW, retransmission mechanism, harvested-energy and prioritised access. The scheme has four completely novel control blocks: RAW size control that adaptively adjusts the RAW sizes according to different number of devices and application types in the networks. RAW retransmission control that improves the channel utilisation by retransmitting the collided packets at the subsequent slot in the same RAW. Harvested-energy powered access control that adjusts the RAW sizes with the consideration of the uncertain amount of harvested-energy in each device and channel conditions. Priority-aware channel access control that reduces the collisions of high-priority packets in the time-critical networks. The performance of the proposed controls is evaluated in Matlab under different net work scenarios. Simulation results show that the proposed controls improve the network performances in terms of energy efficiency, packet delivery ratio and delay as compared to the existing window control

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

Experimental Study on Thermal Performance Improvement of Envelop Integrated with Phase Change Material in Air-conditioned Room

Compared with the traditional building envelope, the thermal mass of building envelope integrated with phase change material is increased greatly, which would reduce the building energy consumption, improve thermal comfort, and shift the peak electricity load. Due to latent heat energy storage when phase changing, the wall integrated with PCM can release heat storage or cold storage to maintain the indoor thermal environment for a period of time after closing the air conditioning. This work presents the results of an experimental study of thermal performances of wall integrated with PCM and without PCM when the air condition runs continuously and intermittently. A building with these two kinds of walls is chosen and the inner surface temperature and heat flow are measured. The building have a domestic heat pump as a cooling system. The results show that the PCM can reduce the inner surface temperature 1℃, and reduce the inner surface heat flow about 40% when the air condition runs continuously. When the air condition runs intermittently by the working schedule, the PCM can also reduce the inner surface temperature 1℃, and the cold storage releasing time of wall integrated with PCM is 2 hours longer than wall without PCM. The PCM can improved the thermal performance of building envelop significantly

Influence of frost damage on water penetration into neat and air entrained concrete

In service life, concrete can be damaged either by mechanical or environmental loads or by combined ones. These damages will strongly influence water movement in concrete which could later lead to more serious deteriorations. This paper applies neutron radiography to investigate the influence of frost damage on water penetration into concrete. In addition, the improvement of frost resistance by addition of air entrainment was investigated. The results indicate that it is possible to visualize penetration of water into the porous structure of concrete by neutron radiography. Further evaluation of the test data allows determining time-dependent moisture profiles quantitatively with high resolution. After concrete is damaged by freeze-thaw cycles water penetration into ordinary concrete is accelerated. It can be shown that frost damage is not equally distributed in specimens exposed to freeze-thaw cycles. Thermal gradients lead to more serious damage near the surface. The beneficial effect of air entrainment on frost resistance has been demonstrated. After 50 freeze-thaw cycles, air entrained concrete showed no measurable increase in water absorption. But layers near the surface of concrete absorbed slightly more water after 200 freeze-thaw cycles although the dynamic elastic modulus remained constant. Results presented in this paper help us to better understand mechanisms of frost damage of concrete

Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models

Entity-level fine-grained sentiment analysis in the financial domain is a crucial subtask of sentiment analysis and currently faces numerous challenges. The primary challenge stems from the lack of high-quality and large-scale annotated corpora specifically designed for financial text sentiment analysis, which in turn limits the availability of data necessary for developing effective text processing techniques. Recent advancements in large language models (LLMs) have yielded remarkable performance in natural language processing tasks, primarily centered around language pattern matching. In this paper, we propose a novel and extensive Chinese fine-grained financial sentiment analysis dataset, FinChina SA, for enterprise early warning. We thoroughly evaluate and experiment with well-known existing open-source LLMs using our dataset. We firmly believe that our dataset will serve as a valuable resource to advance the exploration of real-world financial sentiment analysis tasks, which should be the focus of future research. The FinChina SA dataset is publicly available at https://github.com/YerayL/FinChina-SAComment: FinLLM Symposium at IJCAI 202