Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Likelihood Ratio Based Forensic Text Comparison Procedures: Multivariate Kernel Density with Lexical Features vs. Word N-grams vs. Character N-grams

This is a comparative study to empirically investigate the performances of three different procedures for calculating authorship attribution likelihood ratios (LR). The procedures to be compared are: 1) a procedure based on multivariate kernel density (MVKD) with lexical features; 2) a procedure based on word N-grams; and 3) a procedure based on character N-grams. Furthermore, the best-performing LRs of these three procedures are fused into combined single LRs using a logistic-regression fusion, in order to investigate the extent of the improvement/deterioration that the fusion brings about. This study uses chatlog messages, which were presented as evidence to prosecute paedophiles, for testing. The numbers of word tokens used to model the authorship attribution of each message group are 500 and 1000 words. This was done to examine the effect of sample size on the performance of a system. The performance of a system is assessed with regard to its validity (= accuracy) and reliability (= precision) using the log-likelihood-ratio cost (Cllr) and 95% credible intervals (CI), respectively. While describing the different characteristics of these three procedures in their outcomes, this study demonstrates that the MVKD procedure was the best-performing procedure out of the three in terms of Cllr . This study also demonstrates that a logistic-regression fusion is useful for combining the LRs obtained from the three procedures in question, resulting in a good improvement in performance.
N-(4-Methylphenylsulfonyl)succinamic acid

In the crystal structure of the title compound, C11H13NO5S, the amide C=O and the carboxyl C=O groups of the acid segment orient themselves away from each other. The dihedral angle between the benzene ring and the amide group is 69.0 (2)°. In the crystal, N—H⋯O and O—H⋯O hydrogen bonds link the molecules into layers parallel to the bc plane.
Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation

This paper analyzes a pick-up pattern of taxi service in Jeju area based on the real-life location history data collected from the Taxi Telematics system, aiming at obtaining useful background data necessary to design a location recommendation service for empty taxis. Out of the great amount of location records, pick-up data are extracted by tracing the state change in the predefined taxi state diagram. To decide a reasonable granularity of location recommendation, refined clustering is performed by means of the well-known k-means method supported by the E-Miner statistics software package. In addition, within each cluster, the temporal analysis creates time-dependent pick-up pattern change along the time axis. As a result, the cluster and its spatio-temporal pick-up frequency make it possible to suggest that the empty taxi go to the nearby cluster location, resulting in the reduction of empty taxi ratio.
Post-Layout Simulation Time Reduction for Phase-Locked Loop Frequency Synthesizer Using System Identification Techniques

Compact model extraction of phase-locked loop (PLL) frequency synthesizer using system identification techniques is proposed to reduce post-layout simulation time. This is the first published compact model for PLL using system identification techniques. It features an autoregressive exogenous model for the charge pump and the loop filter with a lookup table for nonlinearity compensation and a radial basis function neural network for the voltage-controlled oscillator with nonlinear frequency-voltage relationship, thereby reducing the post-layout simulation time to 26% of the original circuits with the accuracy of 93%.
Mutagenesis and Expression of amo, Which Codes for Ammonia Monooxygenase in Nitrosomonas europaea

ABSTRACT Nitrosomonas europaea has two copies of the operon encoding ammonia monooxygenase (AMO). The nucleotide sequences of the two copies of amoA were obtained, and they were found to differ by one nucleotide. To determine if both copies ofamoA were functional, insertional mutagenesis was performed to inactivate either copy of amoA alone. A DNA cassette containing the lacZ and kan genes inserted intoamoA was constructed. Mutagenesis was done by using transformation and homologous recombination to mobilize the cassette into the chromosomal copies of amoA. Mutations were obtained in both copies of amoA. Either copy ofamoA was sufficient to support growth when the other copy was disrupted. However, inactivation of one copy of amoA, but not the other, resulted in slower growth. Measurements of ammonia-dependent O2 consumption, which depends on AMO, confirmed that the slower-growing mutant had lower activity while the faster-growing mutant had near wild-type levels of activity. Similarly, as measured by [14C]acetylene label incorporation, there was less active AMO present in the slower-growing mutant than in the faster-growing mutant or in the wild type. Northern blot analysis of transcription likewise showed that the slower-growing mutant had less full-sized AMO mRNA.
Spaced training rescues memory and ERK1/2 signaling in fragile X syndrome model mice

Significance There are no treatments for congenital intellectual disabilities. Here we show that newly discovered timing rules for maximizing hippocampal long-term potentiation predict training regimens that offset defects in synaptic chemistry and memory in the fragile X mental retardation 1 (Fmr1) KO model of fragile X syndrome. Wild-type mice required far less training to form stable memories when given three training trials separated by 1 hour as opposed to one extended session; shorter or longer intervals were ineffective. The same spaced training protocol rescued memory in Fmr1 KO mice and restored activation of synaptic ERK1/2, a kinase critical for both LTP and learning. These results suggest a readily implementable, neurobiologically based therapeutic strategy for a prevalent form of intellectual disability. Recent studies have shown that short, spaced trains of afferent stimulation produce much greater long-term potentiation (LTP) than that obtained with a single, prolonged stimulation episode. The present studies demonstrate that spaced training regimens, based on these LTP timing rules, facilitate learning in wild-type (WT) mice and can offset learning and synaptic signaling impairments in the fragile X mental retardation 1 (Fmr1) knockout (KO) model of fragile X syndrome. We determined that 5 min of continuous training supports object location memory (OLM) in WT but not Fmr1 KO mice. However, the same amount of training distributed across three short trials, spaced by one hour, produced robust long-term memory in the KOs. At least three training trials were needed to realize the benefit of spacing, and intertrial intervals shorter or longer than 60 min were ineffective. Multiple short training trials also rescued novel object recognition in Fmr1 KOs. The spacing effect was surprisingly potent: just 1 min of OLM training, distributed across three trials, supported robust memory in both genotypes. Spacing also rescued training-induced activation of synaptic ERK1/2 in dorsal hippocampus of Fmr1 KO mice. These results show that a spaced training regimen designed to maximize synaptic potentiation facilitates recognition memory in WT mice and can offset synaptic signaling and memory impairments in a model of congenital intellectual disability.
Formate-Dependent Microbial Conversion of CO2 and the Dominant Pathways of Methanogenesis in Production Water of High-temperature Oil Reservoirs Amended with Bicarbonate

CO2 sequestration in deep-subsurface formations including oil reservoirs is a potential measure to reduce the CO2 concentration in the atmosphere. However, the fate of the CO2 and the ecological influences in carbon dioxide capture and storage (CDCS) facilities is not understood clearly. In the current study, the fate of CO2 (in bicarbonate form; 0∼90 mM) with 10 mM of formate as electron donor and carbon source was investigated with high-temperature production water from oilfield in China. The isotope data showed that bicarbonate could be reduced to methane by methanogens and major pathway of methanogenesis could be syntrophic formate oxidation coupled with CO2 reduction and formate methanogenesis under the anaerobic conditions. The bicarbonate addition induced the shift of microbial community. Addition of bicarbonate and formate was associated with a decrease of Methanosarcinales, but promotion of Methanobacteriales in all treatments. Thermodesulfovibrio was the major group in all the samples and Thermacetogenium dominated in the high bicarbonate treatments. The results indicated that CO2 from CDCS could be transformed to methane and the possibility of microbial CO2 conversion for enhanced microbial energy recovery in oil reservoirs.
Exploiting Web Images for Dataset Construction: A Domain Robust Approach

Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time-consuming and labor intensive. To reduce the cost of manual labeling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the “dataset bias problem. ” To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually nonsalient and less relevant expansions are filtered out. By treating each selected expansion as a “bag” and the retrieved images as “instances,” image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison, and object detection demonstrate the domain robustness of our dataset.
Drosophila Salt-inducible Kinase (SIK) Regulates Starvation Resistance through cAMP-response Element-binding Protein (CREB)-regulated Transcription Coactivator (CRTC)*

Salt-inducible kinase (SIK), one of the AMP-activated kinase (AMPK)-related kinases, has been suggested to play important functions in glucose homeostasis by inhibiting the cAMP-response element-binding protein (CREB)-regulated transcription coactivator (CRTC). To examine the role of SIK in vivo, we generated Drosophila SIK mutant and found that the mutant flies have higher amounts of lipid and glycogen stores and are resistant to starvation. Interestingly, SIK transcripts are highly enriched in the brain, and we found that neuron-specific expression of exogenous SIK fully rescued lipid and glycogen storage phenotypes as well as starvation resistance of the mutant. Using genetic and biochemical analyses, we demonstrated that CRTC Ser-157 phosphorylation by SIK is critical for inhibiting CRTC activity in vivo. Furthermore, double mutants of SIK and CRTC became sensitive to starvation, and the Ser-157 phosphomimetic mutation of CRTC reduced lipid and glycogen levels in the SIK mutant, suggesting that CRTC mediates the effects of SIK signaling. Collectively, our results strongly support the importance of the SIK-CRTC signaling axis that functions in the brain to maintain energy homeostasis in Drosophila.
A 1.2V, 2.4GHz Fully Integrated Linear CMOS Power Amplifier with Efficiency Enhancement

A 2.4GHz power amplifier is implemented with standard thin-oxide transistors in a 1.2V, 0.13 mum CMOS process. The output matching network is fully integrated on chip. The PA transmits up to 24dBm linear power with 25% drain efficiency at -1dB compression point. When driven into saturation, it transmits 27dBm peak power with 32% drain efficiency. A technique for enhancing average efficiency is proposed and demonstrated. This technique does not degrade instantaneous efficiency at peak power and maintains constant power gain with power back-off
Title of Document : ENHANCEMENT OF ATMOSPHERIC LIQUID WATER ESTIMATION USING SPACE-BORNE REMOTE SENSING DATA

Title of Document: ENHANCEMENT OF ATMOSPHERIC LIQUID WATER ESTIMATION USING SPACE-BORNE REMOTE SENSING DATA Ruiyue Chen, Doctor of Philosophy, 2010 Directed By: Professor Zhanqing Li Department of Atmospheric and Oceanic Science Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level liquid clouds have the most significant influence on cloud radiative forcing due to their areal extent and frequency. Estimation of atmospheric liquid water contained in low-level clouds and the precipitation underneath them is very important in meteorology, hydrology, and climatology. Space-borne remote sensing data are widely used for global estimation of atmospheric liquid water, given that they have a wider spatial coverage than other data sources and are spanning many years. However, previous space-borne remote sensing techniques have some limitations for estimation of atmospheric liquid water in low-level liquid clouds, namely, the vertical variation of droplet effective radius (DER) is neglected in the calculation of cloud liquid water path (LWP) and the rain underneath low-level liquid clouds can be overlooked. Comprising many state-of-art passive and active instruments, the recently launched NASA A-Train series of satellites provides comprehensive simultaneous information about cloud and precipitation processes. Utilizing A-Train satellite data and ship-borne data from the East Pacific Investigation of Climate (EPIC) campaign, in this study investigated is the estimation of liquid water in low-level liquid clouds, and assessed is the potential of cloud microphysical parameters in the estimation of rain from low-level liquid clouds. This study demonstrates that assuming a constant cloud DER can cause biases in the calculation of LWP. It is also shown that accounting for the vertical variation of DER can reduce the mean biases. This study shows that DER generally increases with height in non-drizzling clouds, consistent with aircraft observations. It is found that in drizzling clouds, the vertical gradient of DER is significantly smaller than that in nondrizzling clouds, and it can become negative when the drizzle is heavier than approximately 0.1 mm hr. It is shown that the warm rain underneath low-level liquid clouds accounts for 45.0% of occurrences of rain and 27.5% of the rainfall amount over the global ocean areas. Passive microwave techniques underestimate the warm rain over oceans by nearly 48%. Among the cloud microphysical parameters, LWP calculated with DER profile shows the best potential for estimating warm rain, which is neglected by traditional techniques of precipitation estimation. ENHANCEMENT OF ATMOSPHERIC LIQUID WATER ESTIMATION USING SPACE-BORNE REMOTE SENSING DATA
Distributed current estimates using cortical orientation constraints

Distributed source models of magnetoencephalographic (MEG) and electroencephalographic (EEG) data employ dense distributions of current sources in a volume or on a surface. Previously, anatomical magnetic resonance imaging (MRI) data have been used to constrain locations and orientations based on cortical geometry extracted from anatomical MRI data. We extended this approach by first calculating cortical patch statistics (CPS), which for each patch corresponding to a current source location on the cortex comprise the area of the patch, the average normal direction, and the average deviation of the surface normal from its average. The patch areas were then incorporated in the forward model to yield estimates of the surface current density instead of dipole amplitudes at the current locations. The surface normal data were employed in a loose orientation constraint (LOC), which allows some variation of the current direction from the average normal. We employed this approach both in the ℓ2 minimum‐norm estimates (MNE) and in the more focal ℓ1 minimum‐norm solutions, the minimum‐current estimate (MCE). Simulations in auditory and somatosensory areas with current dipoles and 10‐ or 20‐mm diameter cortical patches as test sources showed that applying the LOC can increase localization accuracy. We also applied the method to in vivo auditory and somatosensory data. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.
Genomic, Molecular Evolution, and Expression Analysis of Genes Encoding Putative Classical AGPs, Lysine-Rich AGPs, and AG Peptides in Brassica rapa

Arabinogalactan proteins (AGPs) belong to a class of Pro/Hyp-rich glycoproteins and are some of the most complex types of macromolecules found in plants. In the economically important plant species, Brassica rapa, only chimeric AGPs have been identified to date. This has significantly limited our understanding of the functional roles of AGPs in this plant. In this study, 64 AGPs were identified in the genome of B. rapa, including 33 classical AGPs, 28 AG peptides and three lys-rich AGPs. Syntenic gene analysis between B. rapa and A. thaliana suggested that the whole genome triplication event dominated the expansion of the AGP gene family in B. rapa. This resulted in a high retained proportion of the AGP family in the B. rapa genome, especially in the least fractionated subgenome. Phylogenetic and motif analysis classified the classical AGPs into six clades and three orphan genes, and the AG peptides into three clades and five orphan genes. Classical AGPs has a faster rate of molecular evolution than AG peptides revealed by estimation of molecular evolution rates. However, no significant differences were observed between classical AGPs and lys-rich AGPs. Under control conditions and in response to phytohormones treatment, a complete expression profiling experiment has identified five anther-specific AGPs and quite a number of AGPs responding to abscisic acid, methyl jasmonate and/or gibberellin. In this study, we presented a bioinformatics approach to identify important types of AGPs. Moreover, the association between their function and their protein structure, as well as the evolution and the expression of AGP genes were investigated, which might provide fundamental information for revealing the roles of AGPs in B. rapa.
Optimal Rate Adaptation for Hybrid ARQ in Time-Correlated Rayleigh Fading Channels

Most analytical studies on hybrid automatic repeat request (HARQ) have been carried out in either slow fading or fast fading assumptions. However, since practical wireless channels typically exhibit a time-correlated property, we need to take into account the effects of time correlation in the design of HARQ. In this paper, we analyze the outage probability and delay-limited throughput of HARQ with rate adaptation in a time-correlated channel model. Then, we propose a rate adaptation scheme to maximize the delay-limited throughput and evaluate the performance of the proposed scheme under a delay constraint. Our analytical and numerical results show that the proposed rate adaptation scheme outperforms the conventional rate adaptation schemes. The proposed rate adaptation scheme is further simplified by using a Gaussian approximation on the effective channel power gain. The rate adaptation based on the proposed Gaussian approximation reduces the complexity and achieves a throughput gain comparable to that with the exact channel distributions.
Attentional and emotional functioning in schizophrenia patients treated with conventional and atypical antipsychotic drugs

Summary Background Effectiveness of antipsychotics in treating emotional and cognitive deficits in schizophrenia still remains controversial. The aim of our study was to assess emotional and cognitive functioning in schizophrenic inpatients currently treated with typical antipsychotics (perphenazine, perazine, fluphenazine, and haloperidol) and in another group of schizophrenic inpatients currently on atypical antipsychotics (olanzapine, risperidone, amisulpride, and quetiapine). Material/Methods One hundred patients with DSM-IV schizophrenia or schizoaffective disorders (39 treated using typical antipsychotics and 61 treated with atypical antipsychotics) under naturalistic treatment conditions, and 50 healthy controls were given the following: Test of Everyday Attention, Facial Emotion Recognition Test, Facial Memory Recognition Test, and “Reading the mind in the eyes” Test. Results Patients with a diagnosis of schizophrenia revealed the following deficits: facial emotion perception, empathy/theory of mind, visual selective attention/speed, attentional switching, and auditory-verbal working memory. Our results show a significant difference between schizophrenic and healthy controls in all tasks, with schizophrenic patients performing worse than controls. Interestingly, our patients on atypical neuroleptics performed similarly compared to schizophrenic patients treated with conventional neuroleptics on all tasks provided. There were some significant relationships between emotional and cognitive deficits and clinical variables. Conclusions Our findings remain consistent with other recent studies in which atypical antipsychotics did not show a clear advantage over typical antipsychotics on both emotional and cognitive functioning.
Period disambiguation using a neural network

Summary form only given. A problem that has never been addressed in the literature is the problem of machine recognition of sentences in real-world documents (i.e. identifying the beginning and end of a sentence). A description is given of the problem and experiments that show that a feedforward neural network can be trained, using the backpropagation learning algorithm, to disambiguate periods. The authors also present results that indicate that a low training tolerance improves a neural network's ability to generalize at the cost of a dramatic increase in the number of training iterations. <<ETX>>
Evaluating Defect Content Estimation Rules in Software Inspections

This paper is concerned with evaluating two different improvements of an existing defect content estimation model. The model improved is a curve-fitting model. Two new estimation rules are evaluated and compared with the original model. Further, the new estimation rules and the original model are evaluated against one of the most successful defect content estimation models, which is a capture-recapture model. It is concluded that one of the new estimation rules for the curve-fitting model could be a good complement to the capture-recapture model. Moreover, it is concluded that the results support previously published results and hence show strong evidence for that the studied model are mature enough to be transferred to industrial use to support continuous quality assessment and control.