Why are pollution damages lower in developed countries? Insights from high income, high-particulate matter Hong Kong

Conventional wisdom suggests that pollution damages are high in less-developed countries because they are highly polluted. Using administrative data on the universe of births and deaths, we explore the morbidity and mortality effects of gestational particulate matter exposure in high-pollution yet highly-developed Hong Kong. The effects of particulates on birthweight are large. We estimate no effect of particulates on neonatal mortality. We interpret our stark mortality results in a comparative analysis of pollution-mortality relationships across well-known studies. We provide evidence that mortality damages may be high in less-developed countries because they are less developed, not because they are more polluted

Dimensionality's blessing: Clustering images by underlying distribution

Many high dimensional vector distances tend to a constant. This is typically considered a negative "contrast-loss" phenomenon that hinders clustering and other machine learning techniques. We reinterpret "contrast-loss" as a blessing. Re-deriving "contrast-loss" using the law of large numbers, we show it results in a distribution's instances concentrating on a thin "hyper-shell". The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their (unknown) underlying distribution. Distribution-clustering, creates notably clean clusters from raw unlabeled data, estimates the number of clusters for itself and is inherently robust to "outliers" which form their own clusters. This enables trawling for patterns in unorganized data and may be the key to enabling machine intelligence.Comment: Accepted in CVPR 201

Distance Based Image Classification: A solution to generative classification's conundrum?

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory's hierarchical generative process and non-semantic factors by an instance specific noise term. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues. The result is a surprisingly accurate generative classifier, that takes the form of a modified nearest-neighbor algorithm; we term it distance classification. Unlike discriminative classifiers, a distance classifier: defines semantics by what-they-are; is amenable to incremental updates; and scales well with the number of classes.Comment: accepted by IJC

WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing

WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. Although recent studies have begun to investigate WiFi-based multi-user sensing, there remains a lack of benchmark datasets to facilitate reproducible and comparable research. To bridge this gap, we present WiMANS, to our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as well as synchronized videos, monitoring simultaneous activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing.Comment: We present WiMANS, to our knowledge, the first dataset for multi-user activity sensing based on WiF

Predictive model for prolonged hospital stay risk after gastric cancer surgery

BackgroundProlonged postoperative hospital stay following gastric cancer (GC) surgery is an important risk factor affecting patients’ mood and increasing complications. We aimed to develop a nomogram to predict risk factors associated with prolonged postoperative length of stay (PLOS) in patients undergoing gastric cancer resection.MethodsData were collected from 404 patients. The least absolute shrinkage and selection operator (LASSO) was used for variable screening, and a nomogram was designed. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). The consistency between the predicted and actual values was evaluated via a calibration map, and the clinical application value was evaluated via decision curve analysis (DCA) and clinical impact curve analysis (CICA).ResultsA total of 404 patients were included in this study. Among these patients, 287 were assigned to the training cohort, and 117 were assigned to the validation cohort. According to the PLOS quartile distance, 103 patients were defined as having prolonged PLOS. LASSO regression and logistic multivariate analysis revealed that 4 clinical characteristics, the neutrophil–lymphocyte ratio (NLR) on postoperative day one, the NLR on postoperative day three, the preoperative prognostic nutrition index and the first time anal exhaust was performed, were associated with the PLOS and were included in the construction of the nomogram. The AUC of the nomogram prediction model was 0.990 for the training set and 0.983 for the validation set. The calibration curve indicated good correlation between the predicted results and the actual results. The Hosmer-Lemeshow test revealed that the P values for the training and validation sets were 0.444 and 0.607, respectively, indicating that the model had good goodness of fit. The decision curve analysis and clinical impact curve of this model showed good clinical practicability for both cohorts.ConclusionWe explored the risk factors for prolonged PLOS in GC patients via the enhanced recovery after surgery (ERAS) program and developed a predictive model. The designed nomogram is expected to be an accurate and personalized tool for predicting the risk and prognosis of PLOS in GC patients via ERAS measures

Effect of Enhanced Squeezing Needle Structure on the Jetting Performance of a Piezostack-Driven Dispenser.

Advanced dispensing technology is urgently needed to improve the jetting performance of fluid to meet the requirements of electronic product integration and miniaturization. In this work, an on-off valve piezostack-driven dispenser was used as a study object to investigate the effect of needle structure on jetting performance. Based on fluid dynamics, we investigated nozzle cavity pressure and jet velocity during the dispensing process using theoretical simulation for needles with and without a side cap. The results showed that the needle with a side cap had larger jet velocity and was capable of generating 8.27 MPa of pressure in the nozzle cavity, which was 2.39 times larger than the needle without a side cap. Further research on the influence of the nozzle and needle structural parameters showed that a nozzle conic angle of 85°-105°, needle conic angle of 10°-35°, and side clearance of 0.1-0.3 mm produced a dispenser with a large jet velocity and stable performance, capable of dispensing microscale droplets. Finally, a smaller droplet diameter of 0.42 mm was achieved in experiments using a glycerol/ethanol mixture, with a variation range of ± 4.61%