Risk and Predictability of Singapore’s Direct Residential Real Estate Market

This study explores the topic of the predictability of direct real estate prices in the short-run and the risks facing investors via a case study. Two models are estimated using heteroscedastic and autocorrelation robust ML method. Possible structural shifts of the models are examined. The one assuming that the model captures all the economic influences produces slightly better in-sample fitting. The other model assumes that there could be some important information which is not publicly available. Such information can nevertheless be extracted using Kalman filter. The latter has smaller forecast error in general. We found that a rational speculative bubble is an important predictor of short-run price movement, especially when the market is volatile and noisy. Rental is the only fundamental variable which has any important role to play in the short-run price generating process. Further more, the influence of rental is significant only when the market is inactive. Based on the study, we argue that the risk facing market participants comes not from the rational speculative bubble given its predictability, but primarily from unpredictable local policy shifts.Risk; information; rational bubble; Kalman filter

Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition

Facial expression data is characterized by a significant imbalance, with most collected data showing happy or neutral expressions and fewer instances of fear or disgust. This imbalance poses challenges to facial expression recognition (FER) models, hindering their ability to fully understand various human emotional states. Existing FER methods typically report overall accuracy on highly imbalanced test sets but exhibit low performance in terms of the mean accuracy across all expression classes. In this paper, our aim is to address the imbalanced FER problem. Existing methods primarily focus on learning knowledge of minor classes solely from minor-class samples. However, we propose a novel approach to extract extra knowledge related to the minor classes from both major and minor class samples. Our motivation stems from the belief that FER resembles a distribution learning task, wherein a sample may contain information about multiple classes. For instance, a sample from the major class surprise might also contain useful features of the minor class fear. Inspired by that, we propose a novel method that leverages re-balanced attention maps to regularize the model, enabling it to extract transformation invariant information about the minor classes from all training samples. Additionally, we introduce re-balanced smooth labels to regulate the cross-entropy loss, guiding the model to pay more attention to the minor classes by utilizing the extra information regarding the label distribution of the imbalanced training data. Extensive experiments on different datasets and backbones show that the two proposed modules work together to regularize the model and achieve state-of-the-art performance under the imbalanced FER task. Code is available at https://github.com/zyh-uaiaaaa.Comment: Accepted by NeurIPS202

The effect of combination therapy of allicin and fenofibrate on high fat diet-induced vascular endothelium dysfunction and liver damage in rats

<p>Abstract</p> <p>Background</p> <p>It is designed to investigate the effects of combination therapy of allicin and fenofibrate on the endothelial and liver functions in rats with hyperlipidemia.</p> <p>Methods</p> <p>The healthy male Wistar rats fed high fat diet were treated with fenofibrate (80 mg/kg per day) alone, allicin (60 mg/kg per day) alone and a lower dasage of combined therapy (allicin 20 mg/kg per day and fenofibrate 30 mg/kg per day) respectively for 8 weeks. The serum levels of cholesterol, triglyceride, nitrogen oxidative, alanine transferase (ALT) and aspartate transferase (AST) were determined. Acetylcholine-induced endothelium-dependent vascular relaxation (EDVR) of aorta rings was tested, and the morphologic changes of liver tissue were observed.</p> <p>Results</p> <p>Compared with high fat diet control, fenofibrate alone or the combined therapy increased remarkably the levels of high density lipoprotein respectively (P < 0.05). Both single and combined therapy of fenofibrate and allicin significantly enhanced the levels of NO (P < 0.01 or P < 0.05), but the combined therapy had greatest high EDVR responses (P < 0.01). Furthermore, the reduced levels of ALT and AST were significantly obvious in the combined therapy groups (P < 0.01 or P < 0.05). In addition, the lower dosage of combined therapy significantly ameliorated severe fatty degeneration of liver cells occurred in the high fat diet fed rat although the single fenofibrate treatment showed spotty necrosis of liver cells and bile duct expansion.</p> <p>Conclusion</p> <p>Combination therapy with allicin and fenofibrate can effectively enhance the protective effects on endothelial function and reduce the hepatic damage in rats with hyperlipidemia.</p

Fluorescent Nanoparticle-Based Indirect Immunofluorescence Microscopy for Detection of Mycobacterium tuberculosis

A method of fluorescent nanoparticle-based indirect immunofluorescence microscopy (FNP-IIFM) was developed for the rapid detection of Mycobacterium tuberculosis. An anti-Mycobacterium tuberculosis antibody was used as primary antibody to recognize Mycobacterium tuberculosis, and then an antibody binding protein (Protein A) labeled with Tris(2,2-bipyridyl)dichlororuthenium(II) hexahydrate (RuBpy)-doped silica nanoparticles was used to generate fluorescent signal for microscopic examination. Prior to the detection, Protein A was immobilized on RuBpy-doped silica nanoparticles with a coverage of ∼5.1×102 molecules/nanoparticle. With this method, Mycobacterium tuberculosis in bacterial mixture as well as in spiked sputum was detected. The use of the fluorescent nanoparticles reveals amplified signal intensity and higher photostability than the direct use of conventional fluorescent dye as label. Our preliminary studies have demonstrated the potential application of the FNP-IIFM method for rapid detection of Mycobacterium tuberculosis in clinical samples

SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation

In recent years, vision transformers have been introduced into face recognition and analysis and have achieved performance breakthroughs. However, most previous methods generally train a single model or an ensemble of models to perform the desired task, which ignores the synergy among different tasks and fails to achieve improved prediction accuracy, increased data efficiency, and reduced training time. This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation (40 attributes including gender) based on a single Swin Transformer. Our design, the SwinFace, consists of a single shared backbone together with a subnet for each set of related tasks. To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks. Extensive experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks. Especially, it achieves 90.97% accuracy on RAF-DB and 0.22 $\epsilon$-error on CLAP2015, which are state-of-the-art results on facial expression recognition and age estimation respectively. The code and models will be made publicly available at https://github.com/lxq1000/SwinFace

Discovery of diarylpyridine derivatives as novel non-nucleoside HIV-1 reverse transcriptase inhibitors

Two series (4 and 5) of diarylpyridine derivatives were designed, synthesized, and evaluated for anti-HIV-1 activity. The most promising compound, 5e, inhibited HIV-1 IIIB, NL4-3, and RTMDR1 with low nanomolar EC50 values and selectivity indexes of >10,000. The results of this study indicate that diarylpyridine can be used as a novel scaffold to derive a new class of potent NNRTIs, active against both wild-type and drug resistant HIV-1 strains