Evaluating transit-served areas with non-traditional data: An exploratory study of Shenzhen, China

In this study, transit-served areas (TSAs) are defined as areas within a reasonable distance (e.g., 800 meters) of transit services. TSAs have two key dimensions: physical features (e.g., land-use density and mix) and performance (regarding human behaviors). Non-traditional data (NTD) (e.g., social media check-ins and cellular network data) can supplement traditional data (TD) (e.g., interviews and censuses) to enhance studies and monitoring of TSAs. A case study of Shenzhen, China, illustrates how to combine NTD and TD to evaluate the features and performance of 167 TSAs along metro lines. It finds that NTD can be used to formulate new indicators to measure and monitor the two dimensions of TSAs; the features and performance of different TSAs vary significantly; point of interest (POI) efficiency, or the average users attracted by each POI, can be a useful indicator to differentiate TSAs’ performance; the POI efficiency of a single TSA can vary across days and the POI efficiency of an extremely efficient or inefficient TSA can be totally different across days; and the combination of NTD and TD can effectively help locate extreme TSAs and explain factors contributing to the extremity

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered

CDR: Conservative Doubly Robust Learning for Debiased Recommendation

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation

Robust Sequence Networked Submodular Maximization

In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We interweave the robust optimization with the sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithm, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.Comment: 12 pages, 14 figures, aaai2023 conference accepte

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD

Analyzing the underlying relationship between monetary policy and residential property prices in China

Policymakers and the public express concern regarding the volatility of housing prices due to its potential to increase consumer costs and negatively impact housing affordability. Based on empirical study, it has been seen that the expansion of the real estate sector has a significant impact on the investment in fixed assets by firms. This influence is mostly attributed to the alteration of the transmission of monetary policy. Real estate investment is considered a feasible option because of the significant and rapid appreciation in property prices. The primary objective of this study is to examine the influence of monetary policy on the housing market in China. To conduct the current study, macroeconomic data from a total of 44 time periods, ranging from the fourth quarter of 2012 to the fourth quarter of 2022, was collected. The findings of our study indicate that in the context of China, an expansion in the money supply has a greater propensity to positively influence the borrowing activities of real estate suppliers and clients, as opposed to the supply of properties themselves. The housing market can be influenced by governmental actions such as adjustments to the money supply and interest rates. While scholars have extensively examined the subject matter, the housing market in China remains relatively under-researched in terms of its susceptibility to government macroeconomic policies. Moreover, the current study offers a comprehensive overview of the prevailing challenges encountered by the residential property market in China, emphasizing the significance of macroeconomic policies within this particular context

Immune-related adverse events with severe pain and ureteral expansion as the main manifestations: a case report of tislelizumab-induced ureteritis/cystitis and review of the literature

Immune checkpoint inhibitor (ICI) is an up-to-date therapy for cancer with a promising efficacy, but it may cause unique immune-related adverse events (irAEs). Although irAEs could affect any organ, irAEs-induced whole urinary tract expansion was rarely reported. Herein, we reported a 27-year-old male patient with thymic carcinoma who received the treatment of tislelizumab, paclitaxel albumin and carboplatin. He was hospitalized for severe bellyache and lumbago after 6 courses of treatment. Antibiotic and antispasmodic treatment did not relieve his symptoms. The imaging examinations reported whole urinary tract expansion and cystitis. Therefore, we proposed that the patient’s pain was caused by tislelizumab-induced ureteritis/cystitis. After the discontinuation of tislelizumab and the administration of methylprednisolone, his symptoms were markedly alleviated. Herein, we reported a rare case of ICI-induced ureteritis/cystitis in the treatment of thymic cancer and reviewed other cases of immunotherapy-related cystitis and tislelizumab-related adverse events, which will provide a reference for the diagnosis and treatment of ICI-related irAEs