Safety and Cost Performance of Intersection Lighting

It has been reported that nationwide, about one quarter of the roadway travel commonly occurs after dark and half of the roadway traffic fatalities occurred at night. The nighttime traffic crash fatality rate is about three times the daytime traffic crash fatality rate. The problem may become worse at unlit or poorly lit critical roadway safety spots such as interchange, intersections, and railroad and highway crossing, particularly in adverse weather conditions. This study was conducted to investigate the lighting effects on crashes at Indiana intersections. The analysis of Indiana nighttime crash data to identify the contributing factors was conducted. The study intersection sites were selected based on crash frequencies and crash severities. Before and after field light tests were conducted to verify in-service light performance, including illuminance distribution and uniformity ratio. AGi32 simulation was also performed for three selected intersections to compare with field test results. In addition, the long term performance of demonstration luminaires at the I-74 & US 231 interchange was tracked and documented. This activity provides a better understanding of maintenance issues, cycles, and costs. Surveys to both State Highway Agencies (SHAs) and communities were conducted in order to identify perceptions from SHAs and the public toward lighting improvement. The community survey included questions such as the public attitudes to intersection lighting, effectiveness of lighting, visibility improvement, and safety improvement. To quantify safety effects of lighting at intersections, crash modification factors (CMFs) were developed by using two methodologies: before-and-after analysis and cross-sectional statistical analysis. The developed CMFs could be used to justify roadway lighting projects. Life Cycle Cost Analysis (LCCA) was conducted to determine the best lighting solution given a real project scenario. The analysis considered initial (luminaire and installation) cost, operation and maintenance cost, and energy cost

Interactive Visual Reasoning under Uncertainty

One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials. Encountering a novel phenomenon accompanied by ambiguous cause-effect relationships, humans make hypotheses against data, conduct inferences from observation, test their theory via experimentation, and correct the proposition if inconsistency arises. These iterative processes persist until the underlying mechanism becomes clear. In this work, we devise the IVRE (pronounced as "ivory") environment for evaluating artificial agents' reasoning ability under uncertainty. IVRE is an interactive environment featuring rich scenarios centered around Blicket detection. Agents in IVRE are placed into environments with various ambiguous action-effect pairs and asked to determine each object's role. They are encouraged to propose effective and efficient experiments to validate their hypotheses based on observations and actively gather new information. The game ends when all uncertainties are resolved or the maximum number of trials is consumed. By evaluating modern artificial agents in IVRE, we notice a clear failure of today's learning methods compared to humans. Such inefficacy in interactive reasoning ability under uncertainty calls for future research in building human-like intelligence.Comment: Accepted at NeurIPS 2023 (Datasets and Benchmarks

Friction Surface Treatment Selection: Aggregate Properties, Surface Characteristics, Alternative Treatments, and Safety Effects

This study aimed to evaluate the long term performance of the selected surface friction treatments, including high friction surface treatment (HFST) using calcined bauxite and steel slag, and conventional friction surfacing, in particular pavement preservation treatments such as chip seal, microsurfacing, ultrathin bonded wearing course (UBWC), and diamond grinding. This study also attempted to determine the correlation between vehicle crash and pavement surface friction, which makes it possible to quantitatively establish the so-called crash modification factors (CMFs) that are extremely useful in selecting a cost-effective solution to reduce wet pavement vehicle crashes. In-depth reviews were conducted to identify the aspects of the properties for aggregates used in HFST, including aggregate abrasion value (AAV), Los Angeles abrasion (LAA), Micro-Deval abrasion, and polished stone value (PSV). Extensive laboratory testing was conducted to examine the LAA, Micro-Deval abrasion, and PSV, and to provide first-hand data on the calcined bauxite and steel slag that may be used for HFST and friction surfacing in Indiana. Laboratory accelerating polishing was carried out to evaluate the effect of aggregate gradation and identify the HFST systems with satisfactory friction performance with respect to surface macro-texture and friction. Test strips were installed in the pavement on a real-world road to further evaluate the friction performances of the promising HFST systems under the true traffic polishing and assess the potential effect of winter and snow plough. Pull-off testing was also conducted to examine the bonding between the proposed HFST systems and the substrate surface. Field friction test data was utilized to evaluate the long-term friction performances of pavement preservation treatments, including chip seal, microsurfacing, UBWC, and diamond grinding. Statewide vehicle crash data between 2010 and 2014 was examined to determine the crash statistics associated with pavement friction. The crash data was also matched to the annual pavement inventory friction data to quantify the probabilistic association between vehicle crash and pavement friction with respect to interstate, US, and state highways, respectively. Specification requirements were established for the properties of calcined bauxite and steel slag for HFST and friction surfacing with respect to LAA, Micro-Deval abrasion, PSV, Al2O3 content, and fine aggregate angularity (FAA). Specification requirements were also developed for HFST aggregate gradation and surface friction performance. Regression models were developed for predicting the friction numbers of chip seal, microsurfacing, UBWC, and diamond grinding over their service lives. Regression models were also provided to quantify the effectiveness of friction surfacing for interstate, US, and state highways, respectively

Evaluating and Inducing Personality in Pre-trained Language Models

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a principled and quantitative manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. By systematically evaluating LLMs with MPI, we provide the first piece of evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We further devise a Personality Prompting (P^2) method to induce LLMs with specific personalities in a controllable way, capable of producing diverse and verifiable behaviors. We hope this work sheds light on future studies by adopting personality as the essential indicator for various downstream tasks, and could further motivate research into equally intriguing human-like machine behaviors.Comment: Accepted at NeurIPS 2023 (Spotlight

MEWL: Few-shot multimodal word learning with referential uncertainty

Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This word learning capability is believed to be the most fundamental building block of multimodal understanding and reasoning. Despite recent advancements in multimodal learning, a systematic and rigorous evaluation is still missing for human-like word learning in machines. To fill in this gap, we introduce the MachinE Word Learning (MEWL) benchmark to assess how machines learn word meaning in grounded visual scenes. MEWL covers human's core cognitive toolkits in word learning: cross-situational reasoning, bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot benchmark suite consisting of nine tasks for probing various word learning capabilities. These tasks are carefully designed to be aligned with the children's core abilities in word learning and echo the theories in the developmental literature. By evaluating multimodal and unimodal agents' performance with a comparative analysis of human performance, we notice a sharp divergence in human and machine word learning. We further discuss these differences between humans and machines and call for human-like few-shot word learning in machines.Comment: Accepted at ICML 202

Maternal and fetal/neonatal outcomes of pregnancies complicated by pulmonary hypertension: a retrospective study of 154 patients

Objectives: To determine the main clinical and demographic outcomes related to Pulmonary Hypertension (PH) and adverse obstetric and fetal/neonatal outcomes. Methods: This study retrospectively analyzed the medical record data of 154 patients with PH who were admitted to the Third Affiliated Hospital of Guangzhou Medical University between January 2011 and December 2020. Results: According to the severity of elevated Pulmonary Artery Systolic Pressure (PASP), 82 women (53.2%) were included in the mild PH group, 34 (22.1%) were included in the moderate PH group, and 38 (24.7%) were included in the severe PH group. There were significant differences in the incidence of heart failure, premature delivery, Very-Low-Birth-Weight (VLBW) infants, and Small-for-Gestational-Age (SGA) infants among the three PH groups (p < 0.05). Five (3.2%) women died within 7-days after delivery, 7 (4.5%) fetuses died in utero, and 3 (1.9%) neonates died. The authors found that PASP was an independent risk factor for maternal mortality. After adjustment for age, gestational weeks, systolic blood pressure, Body Mass Index (BMI), mode of delivery, and anesthesia, the risk of maternal mortality in the severe PH group was 20.21 times higher than that in the mild-moderate PH group (OR = 21.21 [95% CI 1.7∼264.17]), p < 0.05. All 131 (85.1%) patients were followed up for 12 months postpartum. Conclusions: The authors found that the risk of maternal mortality in the severe PH group was significantly higher than that in the mild-moderate group, highlighting the importance of pulmonary artery pressure screening before pregnancy, early advice on contraception, and multidisciplinary care

Dielectric Breakdown in Chemical Vapor Deposited Hexagonal Boron Nitride

Insulating films are essential in multiple electronic devices because they can provide essential functionalities, such as capacitance effects and electrical fields. Two-dimensional (2D) layered materials have superb electronic, physical, chemical, thermal, and optical properties, and they can be effectively used to provide additional performances, such as flexibility and transparency. 2D layered insulators are called to be essential in future electronic devices, but their reliability, degradation kinetics, and dielectric breakdown (BD) process are still not understood. In this work, the dielectric breakdown process of multilayer hexagonal boron nitride (h-BN) is analyzed on the nanoscale and on the device level, and the experimental results are studied via theoretical models. It is found that under electrical stress, local charge accumulation and charge trapping/detrapping are the onset mechanisms for dielectric BD formation. By means of conductive atomic force microscopy, the BD event was triggered at several locations on the surface of different dielectrics (SiO2, HfO2, Al2O3, multilayer h-BN, and monolayer h-BN); BD-induced hillocks rapidly appeared on the surface of all of them when the BD was reached, except in monolayer h-BN. The high thermal conductivity of h-BN combined with the one-atom-thick nature are genuine factors contributing to heat dissipation at the BD spot, which avoids self-accelerated and thermally driven catastrophic BD. These results point to monolayer h-BN as a sublime dielectric in terms of reliability, which may have important implications in future digital electronic devices.Fil: Jiang, Lanlan. Soochow University; ChinaFil: Shi, Yuanyuan. Soochow University; China. University of Stanford; Estados UnidosFil: Hui, Fei. Soochow University; China. Massachusetts Institute of Technology; Estados UnidosFil: Tang, Kechao. University of Stanford; Estados UnidosFil: Wu, Qian. Soochow University; ChinaFil: Pan, Chengbin. Soochow University; ChinaFil: Jing, Xu. Soochow University; China. University of Texas at Austin; Estados UnidosFil: Uppal, Hasan. University of Manchester; Reino UnidoFil: Palumbo, Félix Roberto Mario. Comisión Nacional de Energía Atómica; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lu, Guangyuan. Chinese Academy of Sciences; República de ChinaFil: Wu, Tianru. Chinese Academy of Sciences; República de ChinaFil: Wang, Haomin. Chinese Academy of Sciences; República de ChinaFil: Villena, Marco A.. Soochow University; ChinaFil: Xie, Xiaoming. Chinese Academy of Sciences; República de China. ShanghaiTech University; ChinaFil: McIntyre, Paul C.. University of Stanford; Estados UnidosFil: Lanza, Mario. Soochow University; Chin

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique

Quantitative Label-Free Proteomic Analysis of Milk Fat Globule Membrane in Donkey and Human Milk

Previous studies have found donkey milk (DM) has the similar compositions with human milk (HM) and could be used as a potential hypoallergenic replacement diet for babies suffering from cow's milk allergy. Milk fat globule membrane (MFGM) proteins are involved in many biological functions, behaving as important indicators of the nutritional quality of milk. In this study, we used label-free proteomics to quantify the differentially expressed MFGM proteins (DEP) between DM (in 4–5 months of lactation) and HM (in 6–8 months of lactation). In total, 293 DEP were found in these two groups. Gene Ontology (GO) enrichment analysis revealed that the majority of DEP participated in regulation of immune system process, membrane invagination and lymphocyte activation. Several significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined for the DEP, such as lysosome, galactose metabolism and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Our study may provide valuable information in the composition of MFGM proteins in DM and HM, and expand our knowledge of different biological functions between DM and HM