Search

In the era of intelligent transportation, driver behavior profiling has become a beneficial technology as it provides knowledge regarding the driver's aggressiveness. Previous approaches achieved promising driver behavior profiling performance through establishing statistical heuristics rules or supervised learning-based models. Still, there exist limits that the practitioner should prepare a labeled dataset, and prior approaches could not classify aggressive behaviors which are not known a priori. In pursuit of improving the aforementioned drawbacks, we propose a novel approach to driver behavior profiling leveraging an unsupervised learning paradigm. First, we cast the driver behavior profiling problem as anomaly detection. Second, we established recurrent neural networks that predict the next feature vector given a sequence of feature vectors. We trained the model with normal driver data only. As a result, our model yields high regression error given a sequence of aggressive driver behavior and low error given at a sequence of normal driver behavior. We figured this difference of error between normal and aggressive driver behavior can be an adequate flag for driver behavior profiling and accomplished a precise performance in experiments. Lastly, we further analyzed the optimal level of sequence length for identifying each aggressive driver behavior. We expect the proposed approach to be a useful baseline for unsupervised driver behavior profiling and contribute to the efficient, intelligent transportation ecosystem., Comment: 11 pages, 2 figures, 1 table, this paper is accepted in WISA 2021

We report novel cosmological constraints obtained from cosmic voids in the final BOSS DR12 dataset. They arise from the joint analysis of geometric and dynamic distortions of average void shapes (i.e., the stacked void-galaxy cross-correlation function) in redshift space. Our model uses tomographic deprojection to infer real-space void profiles and self-consistently accounts for the Alcock-Paczynski (AP) effect and redshift-space distortions (RSD) without any prior assumptions on cosmology or structure formation. It is derived from first physical principles and provides an extremely good description of the data at linear perturbation order. We validate this model with the help of mock catalogs and apply it to the final BOSS data to constrain the RSD and AP parameters $f/b$ and $D_AH/c$, where $f$ is the linear growth rate, $b$ the linear galaxy bias, $D_A$ the comoving angular diameter distance, $H$ the Hubble rate, and $c$ the speed of light. In addition, we include two nuisance parameters in our analysis to marginalize over potential systematics. We obtain $f/b=0.540\pm0.091$ and $D_AH/c=0.588\pm0.004$ from the full void sample at a mean redshift of $z=0.51$. In a flat $\Lambda$CDM cosmology, this implies $\Omega_\mathrm{m}=0.312\pm0.020$ for the present-day matter density parameter. When we use additional information from the survey mocks to calibrate our model, these constraints improve to $f/b=0.347\pm0.023$, $D_AH/c=0.588\pm0.003$, and $\Omega_\mathrm{m}=0.310\pm0.017$. However, we emphasize that the calibration depends on the specific model of cosmology and structure formation assumed in the mocks, so the calibrated results should be considered less robust. Nevertheless, our calibration-independent constraints are among the tightest of their kind to date, demonstrating the immense potential of using cosmic voids for cosmology in current and future data., Comment: 46 pages, 18 figures, 1 table. JCAP accepted version after peer review, typos corrected

This guidebook discusses over two dozen affordable housing tools employed nationally and by ten of Utah's largest cities: Lehi, Ogden, Orem, Park City, Provo, Salt Lake City, Sandy, South Salt Lake, West Jordan, and West Valley City.

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

This guidebook discusses over two dozen affordable housing tools employed nationally and by ten of Utah's largest cities: Lehi, Ogden, Orem, Park City, Provo, Salt Lake City, Sandy, South Salt Lake, West Jordan, and West Valley City.

We report novel cosmological constraints obtained from cosmic voids in the final BOSS DR12 dataset. They arise from the joint analysis of geometric and dynamic distortions of average void shapes (i.e., the stacked void-galaxy cross-correlation function) in redshift space. Our model uses tomographic deprojection to infer real-space void profiles and self-consistently accounts for the Alcock-Paczynski (AP) effect and redshift-space distortions (RSD) without any prior assumptions on cosmology or structure formation. It is derived from first physical principles and provides an extremely good description of the data at linear perturbation order. We validate this model with the help of mock catalogs and apply it to the final BOSS data to constrain the RSD and AP parameters $f/b$ and $D_AH/c$, where $f$ is the linear growth rate, $b$ the linear galaxy bias, $D_A$ the comoving angular diameter distance, $H$ the Hubble rate, and $c$ the speed of light. In addition, we include two nuisance parameters in our analysis to marginalize over potential systematics. We obtain $f/b=0.540\pm0.091$ and $D_AH/c=0.588\pm0.004$ from the full void sample at a mean redshift of $z=0.51$. In a flat $\Lambda$CDM cosmology, this implies $\Omega_\mathrm{m}=0.312\pm0.020$ for the present-day matter density parameter. When we use additional information from the survey mocks to calibrate our model, these constraints improve to $f/b=0.347\pm0.023$, $D_AH/c=0.588\pm0.003$, and $\Omega_\mathrm{m}=0.310\pm0.017$. However, we emphasize that the calibration depends on the specific model of cosmology and structure formation assumed in the mocks, so the calibrated results should be considered less robust. Nevertheless, our calibration-independent constraints are among the tightest of their kind to date, demonstrating the immense potential of using cosmic voids for cosmology in current and future data., Comment: 46 pages, 18 figures, 1 table. JCAP accepted version after peer review, typos corrected

Third world countries tend to have a higher share of plastic waste that is inadequately managed while the First world countries have higher plastic waste generation per person. A difference in the characteristics of plastic pollution depending on the country's standing results in varying optimal recommendations for users. Through Big Text and OSOME meme analysis, I constructed a list with optimal recommendations for First World and Third World countries. Based on the list, I designed a User Interface wit Google Apps Scripts that provide personalized recommendations based on the country's standing and user's preferred difficulty and reassessed the code based on the six qualities of code. The purpose of the User Interface is to aid people who wish to help solve plastic pollution by offering a set of personalized tasks for each user and keeping their progress accountable through a point tracking system. With a significant number of users, the application could eventually contribute to solving the problem of plastic pollution., Comment: 9 pages, 6 figures, 6 tables

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

Often, there are suspicious Amazon reviews that seem to be excessively positive or have been created through a repeating algorithm. I moved to detect fake reviews on Amazon through semantic analysis in conjunction with meta data such as time, word choice, and the user who posted. I first came up with several instances that may indicate a review isn't genuine and constructed what the algorithm would look like. Then I coded the algorithm and tested the accuracy of it using statistical analysis and analyzed it based on the six qualities of code., Comment: 23 pages, 17 figures

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

Hyun Jeong Kim, 1,* Nga Thi Trinh, 1,* Yunjeong Choi, 1 Woorim Kim, 1 Kyung Hyun Min, 1 Sang Oh Kang, 1 Joo Hee Kim, 2 Hyoun-Ah Kim, 3 Ju-Yang Jung, 3 In Ah Choi, 4 Kyung Eun Lee 1 1College of Pharmacy, Chungbuk National University, Cheongju-si, Republic of Korea; 2College of Pharmacy, Ajou University, Suwon, Republic of Korea; 3Department of Rheumatology, Ajou University School of Medicine, Suwon, Republic of Korea; 4Division of Rheumatology, Department of Internal Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea*These authors contributed equally to this workCorrespondence: Kyung Eun LeeCollege of Pharmacy, Chungbuk National University, 660-1 Yeonje-ri, Osong-eup, Heungdeok-gu, Cheongju-si 28160, Republic of KoreaTel +82 43 261 3590Fax +82 43 268 2732Email kaylee@cbnu.ac.krPurpose: TNF-α is a transmembrane protein which requires cleavage by ADAM17 in order to act systemically. The activation of ADAM17 to generate soluble TNF‑α results in an increased inflammatory activity. We hypothesized that variants spanning the ADAM17 gene contribute towards the observed variation in patient response defined by the number of changes in TNF‑α inhibitors.Patients and Methods: Seven single-nucleotide polymorphisms (SNPs) of ADAM17 in 63 patients with rheumatoid arthritis who received TNF-α inhibitors were analyzed: rs57467365, rs62117540, rs117645314, rs6432013, rs532704607, rs117179141, and rs12692386. Univariate and multivariate regression analysis were employed to investigate the independent predictable factors for changes in TNF-α inhibitors.Results: ADAM17 rs117645314 and rs117179141 showed significant association with the number of changes in TNF-α inhibitors. Patients with GA in rs117645314 and AT in rs117179141 had significantly higher chance of two or more changes of TNF-α inhibitors than those with wild homozygous alleles. Multivariate analysis showed that rs117

The influence of plasma-generated reactive species on the conformation of plasmid DNA (pDNA) and the transformation efficiency of Escherichia coli cells were studied. An atmospheric-pressure plasma jet (APPJ) was used to generate reactive oxygen and nitrogen species (RONS) in an aqueous solution. When E. coli cells were transformed, the transformation efficiency of E. coli with the APPJ-treated plasmid was lower than with the APPJ-untreated plasmid. Transformation efficiency was reduced due to structural modification and degradation of the pDNA by the APPJ. Plasma treatment caused structural modification of the plasmid from the supercoiled form to the linear form, and also decreased the amount of plasmid by degrading the deoxyribonucleic acid (DNA) structure accompanied by disruption of nucleobases and DNA strand breakage. The formation of linear plasmid from supercoiled plasmid by the APPJ treatment was verified through electrophoretic analysis of the NdeI restriction enzyme-cut supercoiled plasmid. The structural modification and/or decrease in the amount of pDNA are attributed to the RONS from the plasma itself and to those derived from the interaction of plasma radicals with the aqueous solution. The effect of plasma treatment on the transformation efficiency of E. coli cells was more pronounced with the linear plasmid than with the supercoiled plasmid, indicating that the linear plasmid is more vulnerable to RONS. Overall, these results revealed that plasma-generated RONS can modify the structural and optical properties of bacterial pDNA, thus affecting its biological function.

Electronic health records (EHRs) contain important clinical information about patients. Efficient and effective use of this information could supplement or even replace manual chart review as a means of studying and improving the quality and safety of healthcare delivery. However, some of these clinical data are in the form of free text and require pre-processing before use in automated systems. A common free text data source is radiology reports, typically dictated by radiologists to explain their interpretations. We sought to demonstrate machine learning classification of computed tomography (CT) imaging reports into binary outcomes, i.e. positive and negative for fracture, using regular text classification and classifiers based on topic modeling. Topic modeling provides interpretable themes (topic distributions) in reports, a representation that is more compact than the commonly used bag-of-words representation and can be processed faster than raw text in subsequent automated processes. We demonstrate new classifiers based on this topic modeling representation of the reports. Aggregate topic classifier (ATC) and confidence-based topic classifier (CTC) use a single topic that is determined from the training dataset based on different measures to classify the reports on the test dataset. Alternatively, similarity-based topic classifier (STC) measures the similarity between the reports' topic distributions to determine the predicted class. Our proposed topic modeling-based classifier systems are shown to be competitive with existing text classification techniques and provides an efficient and interpretable representation., Comment: 18 pages

We recently reported that atypical teratoid rhabdoid tumors (ATRTs) comprise at least two transcriptional subtypes with different clinical outcomes; however, the mechanisms underlying therapeutic heterogeneity remained unclear. In this study, we analyzed 191 primary ATRTs and 10 ATRT cell lines to define the genomic and epigenomic landscape of ATRTs and identify subgroup-specific therapeutic targets. We found ATRTs segregated into three epigenetic subgroups with distinct genomic profiles, SMARCB1 genotypes, and chromatin landscape that correlated with differential cellular responses to a panel of signaling and epigenetic inhibitors. Significantly, we discovered that differential methylation of a PDGFRB-associated enhancer confers specific sensitivity of group 2 ATRT cells to dasatinib and nilotinib, and suggest that these are promising therapies for this highly lethal ATRT subtype.

BackgroundThe authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes.ObjectivesTo validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI).MethodsThis was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predict

In this presentation, we briefly review recent investigations on pion and kaon structures from the instanton vacuum. Starting from the low-energy QCD partition function, we have computed the Gasser-Leutwyler low-energy constants, electromagnetic form factors of the pion and kaon, semileptonic decay form factors of the kaon, and light-cone distribution amplitues of the pion and kaon. The results are in good agreement with the experimental and empirical data., Comment: 4 pages, 4 figures. A talk given in the 4th Asia-Pacific Conference on Few-Body Problems in Physics 2008 (APFB08), held during 19-23 August 2008, in Depok, Indonesia

We study the effective chiral Lagrangian in the chiral limit from the instanton vacuum. Starting from the nonlocal effective chiral action, we derive the effective chiral Lagrangian, using the derivative expansion to order $O(p^4)$ in the chiral limit. The low energy constants, $L_1$, $L_2$, and $L_3$ are determined and compared with various models and the corresponding empirical data. The results are in a good agreement with the data. We also discuss about the upper limit of the sigma meson, based on the present results., Comment: 14 pages, 5 figures, submitted to Phys.Rev. D

We study the effective chiral Lagrangian in the chiral limit from the instanton vacuum. Starting from the nonlocal effective chiral action, we derive the effective chiral Lagrangian, using the derivative expansion to order $O(p^4)$ in the chiral limit. The low energy constants, $L_1$, $L_2$, and $L_3$ are determined and compared with various models and the corresponding empirical data. The results are in a good agreement with the data. We also discuss about the upper limit of the sigma meson, based on the present results., Comment: 14 pages, 5 figures, submitted to Phys.Rev. D

Fault and attack survivability issues concerning physical fiber security in all-optical transport networks (AOTNs) require a new approach taking into consideration AOTN physical characteristics. Furthermore, unlike in electronic networks that regenerate signals at every node, attack detection and isolation schemes may not have access to the overhead bits used to transport supervisory information between regenerators or switching sites to perform their functions. This paper presents an analysis of attack and protection problems in AOTNs and proposes a conceptual framework for modeling attack problems and protection schemes for AOTNs., Published in the Proceedings of DARPA Information Survivability Conference and Exposition (DISCEX 2001), June 12-14, 2001, vol. II, pp. 137-145. The original document contains color images.

A key issue in designing software multicast algorithms is to consider the trade-off between performance and portability. Portable software multicast algorithms based on generic communication models cannot capture some architecture-specific features. Without considering the underlying network architecture, these multicast algorithms may not achieve the truly optimal performance when implemented in real networks. The objective of this research is to investigate architecture-dependent tuning on performance of multicast algorithms developed based on architecture-independent models. Specifically, we intend to optimize the multicast algorithm based on the parameterized communication model. We propose two multicast algorithms, OPT-mesh and OPT-min which are the optimized versions of the parameterized multicast algorithm for wormhole-switched mesh networks and BMIN networks, respectively. Using our flit-level simulator the performance of both algorithms are compared with the architecture-independent version of the parameterized multicast algorithm and two other well-known network-dependent algorithms based on the binomial tree.

A key issue in designing software multicast algorithms is to consider the trade-off between performance and portability. Portable software multicast algorithms based on generic communication models cannot capture some architecture-specific features. Without considering the underlying network architecture, these multicast algorithms may not achieve the truly optimal performance when implemented in real networks. The objective of this research is to investigate architecture-dependent tuning on performance of multicast algorithms developed based on architecture-independent models. Specifically, we intend to optimize the multicast algorithm based on the parameterized communication model. We propose two multicast algorithms, OPT-mesh and OPT-min which are the optimized versions of the parameterized multicast algorithm for wormhole-switched mesh networks and BMIN networks, respectively. Using our flit-level simulator the performance of both algorithms are compared with the architecture-independent version of the parameterized multicast algorithm and two other well-known network-dependent algorithms based on the binomial tree.

A key issue in designing software multicast algorithms is to consider the trade-off between performance and portability. Portable software multicast algorithms based on generic communication models cannot capture some architecture-specific features. Without considering the underlying network architecture, these multicast algorithms may not achieve the truly optimal performance when implemented in real networks. The objective of this research is to investigate architecture-dependent tuning on performance of multicast algorithms developed based on architecture-independent models. Specifically, we intend to optimize the multicast algorithm based on the parameterized communication model. We propose two multicast algorithms, OPT-mesh and OPT-min which are the optimized versions of the parameterized multicast algorithm for wormhole-switched mesh networks and BMIN networks, respectively. Using our flit-level simulator the performance of both algorithms are compared with the architecture-independent version of the parameterized multicast algorithm and two other well-known network-dependent algorithms based on the binomial tree.

The vertex- (resp., edge-) deletion graph bipartization problem is the problem of deleting a set of vertices (resp., edges) from a graph so as to make the remaining graph bipartite. In this paper, we first show that the vertex-deletion graph bipartization problem has a solution of size k or less if and only if the edge- deletion graph bipartization problem has a solution of size k or less, when the maximum vertex degree is limited to three. This immediately implies that (1) the vertex-deletion graph bipartization problem is NP-complete for cubic graphs, and (2) the minimum vertex-deletion graph bipartization problem is solvable in polynomial time for planar graphs when the maximum vertex degree is limited to three. We then prove that the vertex- deletion graph bipartization problem is NP-complete for planar graphs when the maximum vertex degree exceeds three. Using this result, we finally show that the via minimization problem, which arises in the design of integrated circuits and printed circuit boards, is NP-complete even when the maximum "junction" degree is limited to four.

El tema de esta tesis es la relación entre la educación de Corea del Sur y la economía surcoreana; la hipótesis, es que el desarrollo en la educación promovió el alto nivel del capital humano, lo que trajo como resultado el crecimiento de la economía en Corea del Sur. El objetivo general es comprender la relación entre la evolución de la educación y el alto crecimiento de la economía en Corea del Sur y la influencia de la educación en la economía. Esto, a través del analisis la influencia de la evolución de la educación en el mercado laboral que requiere capital humano de alto nivel y en el desarrollo de la economía en Corea del Sur.

El tema de esta tesis es la relación entre la educación de Corea del Sur y la economía surcoreana; la hipótesis, es que el desarrollo en la educación promovió el alto nivel del capital humano, lo que trajo como resultado el crecimiento de la economía en Corea del Sur. El objetivo general es comprender la relación entre la evolución de la educación y el alto crecimiento de la economía en Corea del Sur y la influencia de la educación en la economía. Esto, a través del analisis la influencia de la evolución de la educación en el mercado laboral que requiere capital humano de alto nivel y en el desarrollo de la economía en Corea del Sur.