Automated mass appraisal system with cross-city evaluation capability: a test development in China

The appraisal of property value is extremely important in a modern economy. For example, developers and end-consumers use appraisals for their investment decisions. Governments use it for taxation purposes, while banks rely on appraisals to update their risk profile when managing mortgage and credit application activities. With fast developing economies, quickly valuing new cities and suburbs as they get built becomes particularly difficult. Globalisation has also increased the need for common international valuation standards and automated methods. This research investigates the present mass appraisal systems and the role of automated valuation models. Financial institutions and institutional investors are increasingly more concerned about constantly updating their present portfolio value especially in a dynamic market. Trends of significant peaks and troughs need to be accounted in a faster cycle time with short bursts of pricing adjustments. The problem poses a challenge because property transactions are infrequently traded unlike other commodities such as securities. Hence, there are not many recent transactions for the same property to receive an updated value with a simple adjustment based on economic conditions. The study proposes a method that solves both large-scale mass appraisal with an ability to search across cities to discover properties with similar characteristics for its update and comparison scheme. This research advances the automated valuation model for the residential property market with a test development performed in China. In particular, the resulting model was tested with data from Chinese Tier 1, 2 and 3 Cities to evaluate property values. This research performs several major accomplishments. First, it demonstrates the efficacy of reducing human cognitive effort in the mass appraisal exercise. Second, by applying Artificial Neural Network capabilities in the automated valuation model, pricing of residential properties are able to draw upon knowledge from more mature cities with greater number of transactions and apply to newer developments in less developed cities. Third, the proposed mass appraisal system shows the reliability and robustness that matches the rapid development of Chinas real estate market that had been verified by a real application. Finally, the approach developed provides a valuable new method for property valuation that reduces the possible bias, increases consistency and lowers the effort required by current manual methods, with a lower data requirement

Improved weighting in particle filters applied to precise state estimation in GNSS

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%

Enhanced EKF-based Time Calibration for GNSS/UWB Tight Integration

Tight integration of low-cost Ultra-Wide Band (UWB) ranging sensors with mass-market Global Navigation Satellite System (GNSS) receivers is gaining attention as a high-accuracy positioning strategy for consumer applications dealing with challenging environments. However, due to independent clocks embedded in Commercial-Off-The-Shelf (COTS) chipsets, the time scales associated with sensor measurements are misaligned, leading to inconsistent data fusion. Centralized, recursive filtering architectures can compensate for this offset and achieve accurate state estimation. In line with this, a GNSS/UWB tight integration scheme based on an Extended Kalman Filter (EKF) is developed that performs online time calibration of the sensors' measurements by recursively modeling the GNSS/UWB time-offset as an additional unknown in the system state-space model. Furthermore, a double-update filtering model is proposed that embeds optimizations for the adaptive weighting of UWB measurements. Simulation results show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain of 41.60% over a plain EKF integration with uncalibrated time-offset and of 15.43% over the EKF with naive time-offset calibration. Moreover, a real-world experimental assessment demonstrates improved Root-Mean-Square Error (RMSE) performance of 57.58% and 31.03%, respectively

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac

PromptTTS: Controllable Text-to-Speech with Text Descriptions

Using a text description as prompt to guide the generation of text or images (e.g., GPT-3 or DALLE-2) has drawn wide attention recently. Beyond text and image generation, in this work, we explore the possibility of utilizing text descriptions to guide speech synthesis. Thus, we develop a text-to-speech (TTS) system (dubbed as PromptTTS) that takes a prompt with both style and content descriptions as input to synthesize the corresponding speech. Specifically, PromptTTS consists of a style encoder and a content encoder to extract the corresponding representations from the prompt, and a speech decoder to synthesize speech according to the extracted style and content representations. Compared with previous works in controllable TTS that require users to have acoustic knowledge to understand style factors such as prosody and pitch, PromptTTS is more user-friendly since text descriptions are a more natural way to express speech style (e.g., ''A lady whispers to her friend slowly''). Given that there is no TTS dataset with prompts, to benchmark the task of PromptTTS, we construct and release a dataset containing prompts with style and content information and the corresponding speech. Experiments show that PromptTTS can generate speech with precise style control and high speech quality. Audio samples and our dataset are publicly available.Comment: Submitted to ICASSP 202

Mixed comparison of interventions for different exercise types on students with Internet addiction: a network meta-analysis

BackgroundInternet addiction (IA) has a significant negative impact on students. The condition of students with IA can be improved by exercise, which has been identified as an effective intervention strategy. However, the relative effectiveness of different exercise types and the most effective ones remains unknown. This study presents a network meta-analysis to compare six exercise types (team sport, double sport, single sport, team + double sport, team + single sport, and team + double + single sport) based on their effectiveness in reducing Internet addiction and maintaining mental health.MethodsSystematic searches were conducted in PubMed, EMBASE, Cochrane Library, CNKI, Wan Fang, CQVIP, Web of Science, CBM, EBSCO, APA PsycNet, and Scopus, and all relevant studies from the beginning to 15 July 2022 were included on. According to the Cochrane Handbook 5.1.0 Methodological Quality Evaluation Criteria, the listed studies' bias risk was assessed, while the network meta-analysis was performed using STATA 16.0.ResultsA total of 39 randomized controlled trials that met all inclusion criteria including 2,408 students with IA were examined. The meta-analysis results showed that compared with the control group, exercising significantly improved loneliness, anxiety, depression, and interpersonal sensitivity (p < 0.05). Specifically, the network meta-analysis discovered that the single sport, team sport, double sport, team + double sport, and team + double + single sport had significant effects on improving Internet addiction as compared to the respective control group (p < 0.05); the single sport, team sport, and double sport tend to be effective compared with controls in improving mental health (p < 0.05). Compared with the other five types of sports, the double sport was ranked first and showed the greatest potential to be the best choice (cluster ranking value = 3699.73) in improving Internet addiction (SUCRA = 85.5) and mental health (SUCRA = 93.1).ConclusionExercise could be suggested as the best alternative when treating IA in students, based on the extensive positive effects of exercise on IA, anxiety, depression, interpersonal sensitivity, loneliness, and mental health in IA students. Double sport may be the best type of exercise for Internet-addicted students. However, to further examine the benefits of exercise for IA students, more research is required.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=377035, identifier: CRD42022377035

Electrophoretic deposition and laser cladding of bioglass coating on Ti

Bioglass coatings derived from electrophoretic deposition method were fused on Ti surface by laser cladding process using a continuous CO laser. The specimens were studied by field-emission scanning electron microscopy, X-ray diffraction and bonding tests. Titanium oxide layer with hierarchical structures consisting of submicron rows of leaf-like embossments and nano-pores was obtained by combining acid etching and anodization processes, which increased the surface roughness of Ti. When heat-treatment temperature was 700 °C and high, CaSiO phase began to crystallize from the bioglass matrix and the crystallinity reached its maximum at 700 °C. During the electrophoretic deposition process, porous bioglass coatings composed of bioglass particles and fibers were deposited on Ti surface. Bioglass coatings with similar hierarchical structure containing submillimeter bioglass beads and microfibers were synthesized on Ti surface by laser fusion. There are no obvious microcracks at the interface of the Ti-coating, which revealed the good bonding between Ti-porcelain. With the laser scanning distance decreased, the bond strength increased accordingly. After only one day immersion in SBF, calcium phosphate began to precipitate on the bioglass coating's surfaces. The thickness of the calcium phosphate precipitation and the amount of microparticles increased with immersion time

Waterlogging-responsive Genes Revealed by Transcriptome Sequencing in Leaves of Two Crabapple Species with Contrasting Waterlogging Tolerance

Crabapples (Malus sp.) are ornamental woody plants that belong to the Rosaceae family. Flooding has severely hampered the growth and development of crabapple, and little is known about the molecular responses of crabapple to waterlogging tolerance. Cuttings of waterlogging-tolerant Malus hupehensis and waterlogging-intolerant Malus halliana received flooding treatment of 30 days and regular planting, respectively. Using transcriptome sequencing, we isolated 5703 and 2735 waterlogging-responsive genes from waterlogging-treated M. hupehensis and M. halliana leaves. Among these differentially expressed genes (DEGs), only 746 were shared by both. Several variables may explain the greater waterlogging tolerance of M. hupehensis: there were more waterlogging response genes related to carbohydrate and energy metabolism; signal transduction; antioxidation; lipid metabolism; protein and amino acid metabolism; and polysaccharide, cell wall, and cytoskeleton metabolism pathway in the waterlogged leaves of M. hupehensis than in M. halliana. In particular, the number of DEGs related to anaerobic metabolism, fatty acid metabolism, protein phosphorylation and dephosphorylation, γ-aminobutyric acid metabolism and cellulase, pectinase metabolism pathway in the flooded leaves of M. hupehensis was more than that in M. halliana. The alterations in gene expression patterns of the two crabapple species induced by waterlogging varied substantially. These outcomes pave the way for further studies into the functions of genes that may be involved in waterlogging tolerance in crabapples

Confined lithium–sulfur reactions in narrow-diameter carbon nanotubes reveal enhanced electrochemical reactivity

We demonstrate an unusual electrochemical reaction of sulfur with lithium upon encapsulation in narrow-diameter (subnanometer) single-walled carbon nanotubes (SWNTs). Our study provides mechanistic insight on the synergistic effects of sulfur confinement and Li+ ion solvation properties that culminate in a new mechanism of these sub-nanoscale-enabled reactions (which cannot be solely attributed to the lithiation-delithiation of conventional sulfur). Two types of SWNTs with distinct diameters, produced by electric arc (EA-SWNTs, average diameter 1.55 nm) or high-pressure carbon monoxide (HiPco-SWNTs, average diameter 1.0 nm), are investigated with two comparable electrolyte systems based on tetraethylene glycol dimethyl ether (TEGDME) and 1,4,7,10,13-pentaoxacyclopentadecane (15-crown-5). Electrochemical analyses indicate that a conventional solution-phase Li-S reaction occurs in EA-SWNTs, which can be attributed to the smaller solvated [Li(TEGDME)]+ and [Li(15-crown-5)]+ ions within the EA-SWNT diameter. In stark contrast, the Li-S confined in narrower diameter HiPco-SWNTs exhibits unusual electrochemical behavior that can be attributed to a solid-state reaction enabled by the smaller HiPco-SWNT diameter compared to the size of solvated Li+ ions. Our results of the electrochemical analyses are corroborated and supported with various spectroscopic analyses including operando Raman, X-ray photoelectron spectroscopy, and first-principles calculations from density functional theory. Taken together, our findings demonstrate that the controlled solid-state lithiation-delithiation of sulfur and an enhanced electrochemical reactivity can be achieved by sub-nanoscale encapsulation and one-dimensional confinement in narrow-diameter SWNTs.Fil: Fu, Chengyin. University Of California Riverside; Estados UnidosFil: Oviedo, María Belén. University Of California Riverside; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Zhu, Yihan. Zhejiang University Of Technology; ChinaFil: von Wald Cresce, Arthur. U. S. Army Research Laboratory; Estados UnidosFil: Xu, Kang. U. S. Army Research Laboratory; Estados UnidosFil: Li, Guanghui. University Of California Riverside; Estados UnidosFil: Itkis, Mikhail E.. University Of California Riverside; Estados UnidosFil: Haddon, Robert C.. University Of California Riverside; Estados UnidosFil: Chi, Miaofang. Oak Ridge National Laboratory; Estados UnidosFil: Han, Yu. King Abdullah University Of Science And Technology; Arabia SauditaFil: Wong, Bryan M.. University Of California Riverside; Estados UnidosFil: Guo, Juchen. University Of California Riverside; Estados Unido