Towards Efficient Path Query on Social Network with Hybrid RDF Management

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

The development of the quaternion wavelet transform

The purpose of this article is to review what has been written on what other authors have called quaternion wavelet transforms (QWTs): there is no consensus about what these should look like and what their properties should be. We briefly explain what real continuous and discrete wavelet transforms and multiresolution analysis are and why complex wavelet transforms were introduced; we then go on to detail published approaches to QWTs and to analyse them. We conclude with our own analysis of what it is that should define a QWT as being truly quaternionic and why all but a few of the “QWTs” we have described do not fit our definition

Emerging Nonvolatile Memories to Go Beyond Scaling Limits of Conventional CMOS Nanodevices

Continuous dimensional scaling of the CMOS technology, along with its cost reduction, has rendered Flash memory as one of the most promising nonvolatile memory candidates during the last decade. With the Flash memory technology inevitably approaching its fundamental limits, more advanced storage nanodevices, which can probably overcome the scaling limits of Flash memory, are being explored, bringing about a series of new paradigms such as FeRAM, MRAM, PCRAM, and ReRAM. These devices have indeed exhibited better scaling capability than Flash memory while also facing their respective physical drawbacks. The consequent tradeoffs therefore drive the information storage device technology towards further advancement; as a result, new types of nonvolatile memories, including carbon memory, Mott memory, macromolecular memory, and molecular memory have been proposed. In this paper, the nanomaterials used for these four emerging types of memories and the physical principles behind the writing and reading methods in each case are discussed, along with their respective merits and drawbacks when compared with conventional nonvolatile memories. The potential applications of each technology are also briefly assessed

Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios

Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality. To address this shortcoming, ensemble-optimization tries to obtain multiple reasoning paths to get the final answer assembly. However, current ensemble-optimization methods either simply employ rule-based post-processing such as \textit{self-consistency}, or train an additional model based on several task-related human annotations to select the best one among multiple reasoning paths, yet fail to generalize to realistic settings where the type of input questions is unknown or the answer format of reasoning paths is unknown. To avoid their limitations, we propose \textbf{self-agreement}, a generalizable ensemble-optimization method applying in almost all scenarios where the type of input questions and the answer format of reasoning paths may be known or unknown. Self-agreement firstly samples from language model's decoder to generate a \textit{diverse} set of reasoning paths, and subsequently prompts the language model \textit{one more time} to determine the optimal answer by selecting the most \textit{agreed} answer among the sampled reasoning paths. Self-agreement simultaneously achieves remarkable performance on six public reasoning benchmarks and superior generalization capabilities.Comment: Work in progres

High-Efficiency All-Dielectric Metalenses for Mid-Infrared Imaging

Metasurfaces-based flat optics, which can make use of existing foundry planar technology for high-throughput production, allows the arbitrary control of the wavefront and polarization of light within subwavelength thick structures. So far, however, flat optics for the mid-infrared (MIR) has received far less attention than devices operating at visible or near-infrared wavelengths. Here, polarization-insensitive, highly efficient, all-dielectric metalenses operating in the MIR around 4 µm are demonstrated. The metalens is designed using rigorous coupled-wave analysis and is based on hydrogenated amorphous silicon (α-Si:H) nanopillars supported by an MgF2 substrate. The metalenses produce close to a diffraction-limited focal spot and can resolve structures on the wavelength scale where the focusing efficiency reaches 78% at a magnification of 120×. The imaging qualities are comparable with commercial bulk-molded chalcogenide aspheric lenses. These results provide novel solutions for existing MIR technology and nurture new functionalities with the population of miniaturized and planarized optoelectrical devices.The authors acknowledge the facility support from the ANU node of the Australian National Fabrication Facility (ANFF). This work was supported by China Scholarship Council (201506310074); Australian Research Council (ARC) Future Fellowship (FT110100853); and the ARC Centre of Excellence for Ultrahigh Bandwidth Devices for Optical Systems (CE110001018)

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT

Design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking

The current manual spectra acquisition for monitoring water constituents has resulted in discontinuous data acquisition, insufficient amount of data, and small ocean coverage. This article presents the design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking. To ensure that the requirements for spectra acquisition are met, the system is capable of accurately calculating the solar angle by collecting the information of latitude, longitude, date, time and direction, and automatically adjusting the position of instrument observation plane and the pointing angle of fiber optic probe in real-time. It achieves full automation of collecting the downward radiance of skylight, the upward radiance from reference panel and seawater separately through controlling the rotation of fiber optic probe. A 188-day observation experiment was carried out at the coastal ocean experimental station in Qingdao from September 11, 2018 to March 17, 2019. After that, the system was conducted onboard the Dongfanghong 3 scientific research vessel for a one-month demonstration and sea trial in June 2019. Comparative experiments including manual spectra collection, chlorophyll-a sensor measurement, and water samples collection were carried out. The experimental results show that the relative error of the spectra between the system and manual collection is less than 5%, and the relative error of the remote sensing reflectance calculated by the spectra is less than 4%. Considering the chlorophyll-a concentration obtained by the sensor and the water samples as the true value, the relative error of the chlorophyll-a concentration obtained by the system is 10% and 25% respectively. The results show its full and reliable capacity in collecting spectra of seawater automatically and continuously in real-time, with satisfactory accuracy and timeliness