Miracle-dz/UniFAR-FTUs huggingface dataset:
The Completion Agenda Impact on Student Affairs Practice in Community Colleges

ABSTRACT This article considers student affairs practice within the Completion Agenda by studying community college student affairs educators’ beliefs about their work related to the value others attribute to it. The Completion Agenda’s goals are largely economic, and community colleges have been asked to focus on speed rather than the quality of the student experience. Momentum has served as the unifying concept linking recent community college student success literature, and, as a result, student development has become secondary to better completion rates. We approached this research with a belief that student affairs practice contributes to the growth and development of individuals with lifelong value. Neoliberalism, therefore, served as the study’s theoretical framework and challenges a narrative that values college completion for economic means alone. This qualitative study sought to learn from student affairs educators and better understand changes in their work as a result of external forces. Data were gathered from semi-structured interviews with 12 participants in a state with strong community college organization and numerous completion-based initiatives. Documents provided further insight into each institution’s strategies for completion improvement. Themes that arose included participants supporting the Completion Agenda, heightened attention to the role of student affairs related to academic affairs, the importance of college culture, student affairs’ growing student success role at the beginning and end of the student experience, and student affairs occupying a minor place in campus completion plans. The article concludes with limitations, a discussion, and recommendations future research.
Asymptotics of Continuous Bayes for Non-i.i.d. Sources

Clarke and Barron analysed the relative entropy between an i.i. d. source and a Bayesian mixture over a continuous class containing that source. In this paper a comparable result is obtained when the source is permitted to be both non-stationary and dependent. The main theorem shows that Bayesian methods perform well for both compression and sequence prediction even in this most general setting with only mild technical assumptions.
Ethanol Extracts from Mistletoe (Viscum album L.) Act as Natural Antioxidants and Antimicrobial Agents in Uncooked Pork Patties during Refrigerated Storage

The antioxidant potential of mistletoe (Viscum album L. var. coloratum Ohwi; VAL) extract in uncooked pork patties was evaluated. Three concentrations of VAL extract (0.1 [T1], 0.5% [T2] and 1.0% [T3]) along with 0.02% ascorbic acid as a positive control (V) were added to ground pork and pork patties were prepared. Incorporation of VAL extract decreased (p<0.05) the pH of the pork patties throughout the storage time and reduced (p<0.01) the thiobarbituric acid reactive substance values after day 14 of storage. Total plate counts of the VAL extract-treated samples and V-treated samples were also significantly lower (p<0.01) than that of the control (C) throughout the storage period. In addition, odor scores of the VAL extract-treated patties were lower than those of the C- or V-treated samples on 3rd day of the storage period. These results demonstrated that the VAL extract acts as a natural antioxidant in uncooked pork products.
Minimax Robust A Priori Information Aware Channel Equalization

An a priori information aware channel equalizer accepts some prior information about the unknown transmit symbols, in addition to the knowledge of channel coefficients and noise covariance, and therefore offers a superior performance as compared to an equalizer that lacks this additional information. Such an equalizer is employed, for instance, in turbo equalization systems, where it is placed in conjunction with a channel decoder that provides the necessary a priori information. In this paper, we consider the design of such an a priori information aware equalizer, under an imperfect knowledge of channel parameters. Precisely speaking, we pursue a minimax optimization procedure to robustify the design of the equalizer in presence of an uncertainty about the channel coefficients as well as the interference plus noise covariance. To this end, we employ a Kullback-Leibler divergence based, and a norm based uncertainty class, obtain closed form expressions for the worst-case uncertainties, and finally arrive at the optimal mean-square error (MSE) equalizers that offer the best worst-case performance under uncertainty. Finally, we show that the proposed minimax robust equalizer achieves significant performance gains in comparison to the conventional (nonrobust) equalizer, both as a standalone entity and also as part of an iterative detection system.
Enhancing CCTV: Averages improve face identification from poor-quality images

Low‐quality images are problematic for face identification, for example, when the police identify faces from CCTV images. Here, we test whether face averages, comprising multiple poor‐quality images, can improve both human and computer recognition. We created averages from multiple pixelated or nonpixelated images and compared accuracy using these images and exemplars. To provide a broad assessment of the potential benefits of this method, we tested human observers (n = 88; Experiment 1), and also computer recognition, using a smartphone application (Experiment 2) and a commercial one‐to‐many face recognition system used in forensic settings (Experiment 3). The third experiment used large image databases of 900 ambient images and 7,980 passport images. In all three experiments, we found a substantial increase in performance by averaging multiple pixelated images of a person's face. These results have implications for forensic settings in which faces are identified from poor‐quality images, such as CCTV
A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525–0.605 μm) and SWIR (Band 9: 1.550–1.750 μm) bands of the ALI sensor, namely NDWIA4,9, was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWIA4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy.
Inferring Interdomain Congestion on Links Involving a Residential Broadband Provider in the United States

We aim to illuminate tensions between Internet service providers (ISPs) by providing data on the existence of persistently congested interdomain links in the United States; and by creating a new congestion map of the Internet, with particular emphasis on interconnection links involving residential providers. Combined, these findings will: • Provide evidence regarding the health of residential connectivity. • Identify any actors (or groups of actors) persistently running congested links. • Identify and characterize events of high congestion across providers. • Explore the impact of interdomain congestion on end-to-end latency, a leading determinant of end-user quality of service [1]. This project's broad scope appeals to the interests of at least three groups. First, individual consumers and their advocacy organizations, concerned with events impacting performance of their broadband connection 1. Second, federal regulators, interested in preventing anti-competitive practices and consumer harms. Third, the Internet measurements research community, which would gain access to our datasets collected from a distributed, nationwide network. Finally, though broadband and content providers have access to private, massive datasets already, several of them have agreed to provide us with data to confirm our inferences. Our exploration of interdomain congestion faces two main challenges. First, the nature of tussles between Internet service providers (ISPs) is not well understood, given that non-disclosure agreements (NDA) protect peering contracts. And second, there is insufficient network measurements data available to study interconnection disputes [7]. We use a combination of topology probes [citation pending] and latency measurements from residential vantage points to circumvent these challenges. Specifically, we deploy measurement software to access network nodes, in order to both detect interdomain congestion and characterize its potential impact on QoS. Additionally, we leverage data analysis techniques on existing latency 1 As evidenced by the high volume of comments the Federal Communications Commission got on proposed changes to broadband policy [2]. data collected using the CAIDA Ark infrastructure [8], combined with topological data, to develop a congestion " heat map " of the Internet.
Deciphering the Cosmic Star Formation History and the Nature of Type Ia Supernovae with Future Supernova Surveys

We investigate the prospects of future supernova searches getting meaningful constraints on the cosmic star formation history (CSFH) and the delay time of Type Ia supernovae from star formation (τIa), based only on supernova data. Here we parameterized the CSFH by two parameters, α and β, which are the evolutionary indices [∝(1 + z){α,β}] at z ≳ 1 and ≲1, respectively, and quantitatively examined how well the three parameters (α, β, and τIa) can be constrained in ongoing and future supernova surveys. We found that the type classification of detected supernovae down to the magnitude of IAB ~ 27 is essential for getting a useful constraint on β. The parameter τIa can also be constrained to within an accuracy of ~1-2 Gyr without knowing α, which is somewhat degenerate with τIa. This might be achieved by ground-based surveys but depends on the still highly uncertain type classification by imaging data. More reliable classification will be achieved by the SNAP mission. The supernova counts at a magnitude level of IAB or KAB ~ 30 will allow us to break degeneracies between α and τIa and independently constrain all three parameters, even without knowing supernova types. This can be achieved by the SNAP and JWST missions, which have the different strengths of larger statistics and reaching to higher redshifts, respectively. The dependence of observable quantities on survey time intervals is also quantitatively calculated and discussed.
The Economic Value of Realized Volatility

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. A few recent studies also find that density forecasts based on realized volatility are superior to those based on daily data. We investigate whether these forecasting improvements translate into economic value added. In order to address this question we develop a new class of discrete-time option valuation models that use daily returns as well as realized volatility, and that nest the daily Heston and Nandi (2000) GARCH model as a special case. We derive closed-form option valuation formulas and we assess the option valuation properties using S&P500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity and volatility levels.
Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

A primary criterion of wireless sensor network is energy efficiency. Focused on the energy problem of target tracking in wireless sensor networks, this paper proposes a cluster-based dynamic energy management mechanism. Target tracking problem is formulated by the multi-sensor detection model as well as energy consumption model. A distributed adaptive clustering approach is investigated to form a reasonable routing framework which has uniform cluster head distribution. Dijkstra's algorithm is utilized to obtain optimal intra-cluster routing. Target position is predicted by particle filter. The predicted target position is adopted to estimate the idle interval of sensor nodes. Hence, dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that the operation energy consumption of wireless sensor network can be reduced. The sensor nodes around the target wake up on time and act as sensing candidates. With the candidate sensor nodes and predicted target position, the optimal sensor node selection is considered. Binary particle swarm optimization is proposed to minimize the total energy consumption during collaborative sensing and data reporting. Experimental results verify that the proposed clustering approach establishes a low-energy communication structure while the energy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energy management.
Lung Infections Associated with Cystic Fibrosis

SUMMARY While originally characterized as a collection of related syndromes, cystic fibrosis (CF) is now recognized as a single disease whose diverse symptoms stem from the wide tissue distribution of the gene product that is defective in CF, the ion channel and regulator, cystic fibrosis transmembrane conductance regulator (CFTR). Defective CFTR protein impacts the function of the pancreas and alters the consistency of mucosal secretions. The latter of these effects probably plays an important role in the defective resistance of CF patients to many pathogens. As the modalities of CF research have changed over the decades from empirical histological studies to include biophysical measurements of CFTR function, the clinical management of this disease has similarly evolved to effectively address the ever-changing spectrum of CF-related infectious diseases. These factors have led to the successful management of many CF-related infections with the notable exception of chronic lung infection with the gram-negative bacterium Pseudomonas aeruginosa. The virulence of P. aeruginosa stems from multiple bacterial attributes, including antibiotic resistance, the ability to utilize quorum-sensing signals to form biofilms, the destructive potential of a multitude of its microbial toxins, and the ability to acquire a mucoid phenotype, which renders this microbe resistant to both the innate and acquired immunologic defenses of the host.
A new FMH filter algorithm for removing impulse noise

The proposed novel algorithm operates on the output of the FMH filter and reduces the averaged impulse type noise in speech/audio signals effectively and reduces jitter. The algorithm estimates the likelihood of the sample under inspection is corrupt relative to the neighboring samples and replaces a sample detected as corrupted by a value based on the neighboring samples. In the proposed algorithm there is no need to preset a threshold value to detect the averaged-impulse-noise. The algorithm has the advantage of being configurable to the type of noise impulses in the sample, as the threshold used to detect noise impulse varies to suit the signal.
Transcriptional regulation of the proto‐oncogene Zfp521 by SPI1 (PU.1) and HOXC13

The mouse zinc‐finger gene Zfp521 (also known as ecotropic viral insertion site 3; Evi3; and ZNF521 in humans) has been identified as a B‐cell proto‐oncogene, causing leukemia in mice following retroviral insertions in its promoter region that drive Zfp521 over‐expression. Furthermore, ZNF521 is expressed in human hematopoietic cells, and translocations between ZNF521 and PAX5 are associated with pediatric acute lymphoblastic leukemia. However, the regulatory factors that control Zfp521 expression directly have not been characterized. Here we demonstrate that the transcription factors SPI1 (PU.1) and HOXC13 synergistically regulate Zfp521 expression, and identify the regions of the Zfp521 promoter required for this transcriptional activity. We also show that SPI1 and HOXC13 activate Zfp521 in a dose‐dependent manner. Our data support a role for this regulatory mechanism in vivo, as transgenic mice over‐expressing Hoxc13 in the fetal liver show a strong correlation between Hoxc13 expression levels and Zfp521 expression. Overall these experiments provide insights into the regulation of Zfp521 expression in a nononcogenic context. The identification of transcription factors capable of activating Zfp521 provides a foundation for further investigation of the regulatory mechanisms involved in ZFP521‐driven cell differentiation processes and diseases linked to Zfp521 mis‐expression.
Electron propagation in crossed magnetic and electric fields

We analyse the dynamics of monoenergetic electrons in the presence of uniform, perpendicular magnetic and electric fields. The Green function technique is used to derive analytic results for the field-induced quantum mechanical drift motion of (i)? single electrons and (ii)? a dilute Fermi gas of electrons. The method yields the drift current and, at the same time it allows us to quantitatively establish the broadening of the (magnetic) Landau levels due to the electric field: level number k is split into k+1 sublevels that render the kth oscillator eigenstate in energy space. Adjacent Landau levels will overlap if the electric field exceeds a critical strength. Our analysis suggests an alternative mechanism of conductivity quantization in the quantum Hall effect. The results are of particular relevance in strong current configurations where the presence of a perpendicular electric field must be taken into account.
ELF-2, a new member of the Eph ligand family, is segmentally expressed in mouse embryos in the region of the hindbrain and newly forming somites

The Eph receptors are the largest known family of receptor tyrosine kinases and are notable for distinctive expression patterns in the nervous system and in early vertebrate development. However, all were identified as orphan receptors, and only recently have there been descriptions of a corresponding family of ligands. We describe here a new member of the Eph ligand family, designated ELF-2 (Eph ligand family 2). The cDNA sequence for mouse ELF-2 indicates that it is a transmembrane ligand. It shows closest homology to the other known transmembrane ligand in the family, ELK-L/LERK-2/Cek5-L, with 57% identity in the extracellular domain. There is also striking homology in the cytoplasmic domain, including complete identity of the last 33 amino acids, suggesting intracellular interactions. On cell surfaces, and in a cell-free system, ELF-2 binds to three closely related Eph family receptors, Elk, Cek10 (apparent ortholog of Sek-4 and HEK2), and Cek5 (apparent ortholog of Nuk/Sek-3), all with dissociation constants of approximately 1 nM. In situ hybridization of mouse embryos shows ELF-2 RNA expression in a segmental pattern in the hindbrain region and the segmenting mesoderm. Comparable patterns have been described for Eph family receptors, including Sek-4 and Nuk/Sek-3, suggesting roles for ELF-2 in patterning these regions of the embryo.
Contagion in social attitudes about prejudice

Abstract Members of the same household share similar social attitudes, but the source of the similarity in attitudes may be attributed to many processes. This study uses data from a randomized field experiment to identify contagion in attitude change about anti-transgender prejudice. During a face-to-face canvassing experiment, registered voters who answered the door were exposed to either a message encouraging active perspective taking intended to reduce transphobia or a recycling message. Here, I show that the messages delivered to one household member are likely to reduce anti-transgender prejudice in the cohabitants as well. This finding suggests that door-to-door canvassing messages intended to elicit attitude change are likely to be socially transmitted.
Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley 1

Saleh, Dina K. , David L. Lorenz, and Joseph L. Domagalski, 2010. Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley. Journal of the American Water Resources Association (JAWRA) 00(0):1‐11. DOI: 10.1111/j.1752‐1688.2010. 00506.x
Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms

Respiratory motion can cause artifacts in myocardial SPECT. The authors incorporate respiratory mechanics into the current 4D MCAT and into the next generation spline-based MCAT phantoms. In order to simulate respiratory motion in the current MCAT phantom, the geometric solids for the diaphragm, heart, ribs, and lungs were altered through manipulation of parameters defining them. Affine transformations were applied to the control points defining the same respiratory structures in the spline-based MCAT phantom to simulate respiratory motion. The NURBS surfaces for the lungs and body outline were constructed in such a way as to be linked to the surrounding ribs. Expansion and contraction of the thoracic cage then coincided with expansion and contraction of the lungs and body. The changes both phantoms underwent were splined over time to create time continuous 4D respiratory models. It is concluded that both respiratory models are effective tools for researching effects of respiratory motion.
T-Lohi: A New Class of MAC Protocols for Underwater Acoustic Sensor Networks

This paper introduces T-Lohi, a new class of distributed and energy-efficient media-access protocols (MAC) for underwater acoustic sensor networks (UWSN). MAC design for UWSN faces significant challenges. For example, acoustic communication suffers from latencies five orders-of-magnitude larger than radio communication, so a naive CSMA MAC would require very long listen time resulting in low throughput and poor energy efficiency. In this paper, we first identify unique characteristics in underwater networking that may affect all MACs, such as space-time uncertainty and deafness conditions. We then develop T-Lohi employing a novel tone-based contention resolution mechanism that exploits space-time uncertainty and high latency to detect collisions and count contenders, achieving good throughput across all offered loads. Lohi uses our low-power wake-up receiver to significantly reduce energy consumption. Finally, we evaluate design choices and protocol performance through extensive simulation. The results show that the energy cost of packet transmission is within 3-9 % of optimal, and that Lohi achieves good channel utilization, within 30% utilization of the theoretical maximum. We also show that Lohi is stable and fair under both low and very high offered loads.