SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal Prediction

Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation. In typical implementations of an SNN classifier, decisions are produced after the entire input sequence has been processed, resulting in latency and energy consumption levels that are fairly uniform across inputs. Recently introduced delay-adaptive SNNs tailor the inference latency -- and, with it, the energy consumption -- to the difficulty of each example, by producing an early decision when the SNN model is sufficiently ``confident''. In this paper, we start by observing that, as an SNN processes input samples, its classification decisions tend to be first under-confident and then over-confident with respect to the decision's ground-truth, unknown, test accuracy. This makes it difficult to determine a stopping time that ensures a desired level of accuracy. To address this problem, we introduce a novel delay-adaptive SNN-based inference methodology that, wrapping around any pre-trained SNN classifier, provides guaranteed reliability for the decisions produced at input-dependent stopping times. The approach entails minimal added complexity as compared to the underlying SNN, requiring only thresholding and counting operations at run time, and it leverages tools from conformal prediction (CP).Comment: Under revie

Impact of dyslipidemia on the severity of symptomatic lumbar spine degeneration: A retrospective clinical study

BackgroundLumbar intervertebral disc degeneration (IVDD) is an important cause of low back pain or sciatica, and metabolic factors play an important role. However, little is known about the relationship of dyslipidemia to the risk of intervertebral disc degeneration (IVDD). This study aimed to assess the impact of serum lipid levels on the severity of lumbar disc degeneration and to investigate its association with endplate inflammation.MethodsWe conducted a case retrospective study in which a total of 302 hospitalized Chinese patients were recruited, of whom 188 (112 males and 76 females; mean age: 51.66 years) were without underlying disease, while the remaining 114 patients (51 males and 63 females; mean age: 62.75 years) had underlying diseases. We examined fasting serum lipid levels for total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Magnetic resonance imaging (MRI) was used to determine endplate inflammation. Pfirrmann grading and Weishaupt grading were used to evaluate the severity of intervertebral disc degeneration and facet joint degeneration, respectively.ResultsThere was no difference in age, gender, and general BMI between the two groups (P > 0.05), but there were significantly high levels in TC, LDL-C, and LDL-C/HDL-C (P = 0.04, P = 0.013, P = 0.01, respectively). TG and HDL-C showed no significant difference (P = 0.064, P = 0.336, respectively). The multivariate logistic regression model showed that age was a risk factor for the occurrence of endplate inflammation. In the group without underlying diseases, age, but not other indicators, was a risk factor for the occurrence of endplate inflammation (P < 0.01), In the group with underlying diseases, none of the patient indicators was directly related to the occurrence of endplate inflammation (P > 0.05). A nonlinear machine learning model was used to measure the contribution of each factor to the disease outcome and to analyze the effect between the top three contributing factors and the outcome variables. In patients without underlying diseases, the top three factors contributing to the severity grading of intervertebral disc degeneration were age (32.9%), high-density lipoproteins (20.7%), and triglycerides (11.8%). For the severity grading of facet joint degeneration, the top three contributing factors were age (27.7%), high-density lipoproteins (19.4%), and triglycerides (14.6%). For patients with underlying diseases, the top three factors contributing to intervertebral disc degeneration were age (25.4%), BMI (15.3%), and low-density lipoprotein/high-density lipoprotein ratio (13.9%). In terms of degree classification for facet joint degeneration, the top three contributing factors were age (17.5%), BMI (17.2%), and total cholesterol (16.7%).ConclusionThis study shows that age, high-density lipoprotein, and triglycerides affect the degree of degeneration in patients with symptomatic lumbar degeneration without underlying diseases. Age and BMI are two major factors affecting the severity of degeneration in patients with underlying diseases, and dyslipidemia is a secondary factor. However, there is no clear association between dyslipidemia and the occurrence of endplate inflammation in either group

Quantification of airway thickness changes in smoke-inhalation injury using in-vivo 3-D endoscopic frequency-domain optical coherence tomography

Smoke inhalation injury is frequently accompanied by cyanide poisoning that may result in substantial morbidity and mortality, and methods are needed to quantitatively determine extent of airway injury. We utilized a 3-D endoscopic frequency-domain optical coherence tomography (FD-OCT) constructed with a swept-source laser to investigate morphological airway changes following smoke and cyanide exposure in rabbits. The thickness of the mucosal area between the epithelium and cartilage in the airway was measured and quantified. 3-D endoscopic FD-OCT was able to detect significant increases in the thickness of the tracheal walls of the rabbit beginning almost immediately after smoke inhalation injuries which were similar to those with combined smoke and cyanide poisoning

Toward sustainable management of phosphorus flows in a changing rural–urban environment: recent advances, challenges, and opportunities

While food is mainly produced in rural areas, the main drivers of consumption are urban environments. The increasing demand for food in cities leads to an accumulation of phosphorus (P) in urban environments, which affects P flows at multiple scales. This study reviewed recent advances, challenges, and opportunities for sustainable management of P flows in decoupled rural–urban environments. We discussed recent advances in Substance Flow Analysis (SFA) that enable the characterization of P flows in rural–urban environments and illustrate how urban consumption affects rural food production systems. Most challenges of SFA are associated with the (1) spatial decoupling of food production in rural areas and food consumption in urban systems at multiple scales, (2) temporal implications of substance flows on system analysis, and (3) increasing complexity of resource flows in rural–urban environments. We identified three main opportunities for future research on sustainable management of P flows. These opportunities lie in linking urban and rural SFA through multi-scale analysis, increasing both spatial and temporal resolution of P flows within various environments and identifying how linking urban to rural environments can reduce pressure on primary production systems. Also, we highlighted that methodological development on high-resolution SFA at multiple scales is needed to close P cycles between rural and urban systems and to allow the development of future, sustainable P management systems in the anthropogenic food production chain.</p

Determining intra-urban spatial accessibility disparities in multimodal public transport networks

Urban rail systems have been added to public transport systems, thereby changing distribution disparities in urban spatial accessibility. These disparities reflect both the ability of the public transport system to meet the needs of residents and the locational pros and cons of public service facilities. In this paper, integrated accessibility metrics are used to assess the disparities in Nanjing, Jiangsu Province, China. This is achieved by dividing the urban space into a multilevel grid that can be easily combined with grid-based population data to facilitate accessibility modeling, calculation, and evaluation. Additionally, an acquisition method for more accurate travel time data in the multimodal public transportation network was developed on the basis of an Internet mapping service. This provides a realistic, multimodal, door-to-door modeling approach that avoids the requirement of building complex traffic networks through Geographic Information System (GIS) software and simplifies road network modeling efforts. The results show that this modeling method can be used to reflect the accessibility disparities in the Nanjing urban space objectively and accurately

A Quantitative Flood-Related Building Damage Evaluation Method Using Airborne LiDAR Data and 2-D Hydraulic Model

The evaluation of building damage is of great significance for flood management. Chinese floodplains usually contain small- and medium-sized towns with many other scattered buildings. Detailed building information is usually scarce, making it difficult to evaluate flood damage. We developed an evaluation method for building damage by using airborne LiDAR data to obtain large-area, high-precision building information and digital elevation models (DEMs) for potentially affected areas. These data were then used to develop a two-dimensional (2-D) flood routing model. Next, flood loss rate curves were generated by fitting historical damage data to allow rapid evaluation of single-building losses. Finally, we conducted an empirical study based on the Gongshuangcha detention basin in China&#8217;s Dongting Lake region. The results showed that the use of airborne LiDAR data for flood-related building damage evaluation can improve the assessment accuracy and efficiency; this approach is especially suitable for rural areas where building information is scarce