Miracle-dz/UniFAR-FTUs huggingface dataset:
Internal nitrogen removal from sediments by the hybrid system of microbial fuel cells and submerged aquatic plants

Sediment internal nitrogen release is a significant pollution source in the overlying water of aquatic ecosystems. This study aims to remove internal nitrogen in sediment-water microcosms by coupling sediment microbial fuel cells (SMFCs) with submerged aquatic plants. Twelve tanks including four treatments in triplicates were designed: open-circuit (SMFC-o), closed-circuit (SMFC-c), aquatic plants with open-circuit (P-SMFC-o) and aquatic plants with closed-circuit (P-SMFC-c). The changes in the bio-electrochemical characteristics of the nitrogen levels in overlying water, pore water, sediments, and aquatic plants were documented to explain the migration and transformation pathways of internal nitrogen. The results showed that both electrogenesis and aquatic plants could facilitate the mineralization of organic nitrogen in sediments. In SMFC, electrogenesis promoted the release of ammonium from the pore water, followed by the accumulation of ammonium and nitrate in the overlying water. The increased redox potential of sediments due to electrogenesis also contributed to higher levels of nitrate in overlying water when nitrification in pore water was facilitated and denitrification at the sediment-water interface was inhibited. When the aquatic plants were introduced into the closed-circuit SMFC, the internal ammonium assimilation by aquatic plants was advanced by electrogenesis; nitrification in pore water and denitrification in sediments were also promoted. These processes might result in the maximum decrease of internal nitrogen with low nitrogen levels in the overlying water despite the lower power production. The P-SMFC-c reduced 8.1%, 16.2%, 24.7%, and 25.3% of internal total nitrogen compared to SMFC-o on the 55th, 82th, 136th, and 190th days, respectively. The smaller number of Nitrospira and the larger number of Bacillus and Pseudomonas on the anodes via high throughput sequencing may account for strong mineralization and denitrification in the sediments under closed-circuit. The coupled P-SMFC system has shown good potential for the efficient removal of internal nitrogen.
On approximate KKT condition and its extension to continuous variational inequality problems ∗

In this work we introduce a necessary natural sequential Approximate-Karush-Kuhn-Tucker (AKKT) condition for a point to be a solution of a continuous variational inequality problem without constraint qualifications, and we prove its relation with the Approximate Gradient Projection condition (AGP) of Gárciga-Otero and Svaiter. We also prove that a slight variation of the AKKT condition is sufficient on a convex problem, and we prove sufficiency results for the AKKT condition on convex optimization problems. Sequential necessary conditions are more suitable to iterative methods than usual punctual conditions relying on constraint qualifications.
Co-production, the third sector and the delivery of public services

Abstract In recent years, public management research has paid increasing attention to the third sector, especially to its role in the provision of public services. Evidence of this is the rising number of publications on the topic, as well as a growing number of sessions and papers on the topic in academic conferences of the EGPA and IRSPM. However, much of the discussion on its role is motivated at least as much by ideology as by fact. We still lack a comprehensive empirical understanding of what happens when the third sector is drawn into public service provision. In this collection on Co-Production: The Third Sector and the Delivery of Public Services, we will try to enhance this understanding by presenting several new studies on the subject. We also introduce the concepts of co-production, co-management and co-governance as a conceptual framework that enables us to better understand such developments.
Essential Role for Synaptopodin in Dendritic Spine Plasticity of the Developing Hippocampus

Dendritic spines are a major substrate of brain plasticity. Although many studies have focused on Ca2+/calmodulin-dependent protein kinase II (CaMKII)-mediated regulation of spine dynamics and synaptic function in adult brain, much less is know about protein kinase A (PKA)-dependent regulation of spine shape dynamics during postnatal brain development. Synaptopodin is a dendritic spine associated modulator of actin dynamics and a substrate of PKA. Here we show that NMDA and cAMP-induced dendritic spine expansion is impaired in hippocampal slices from 15- and 21-d-old synaptopodin-deficient mice. We further show that synaptopodin is required for full expression of PKA-dependent hippocampal long-term potentiation in 15- and 21-d-old, but not adult, mice. PKA-induced cAMP response element-binding phosphorylation is normal in the hippocampus of synaptopodin-deficient mice, suggesting that synaptopodin functions independently of cAMP response element-binding. Our results identify synaptopodin as a substrate of PKA in hippocampal neurons and point to an essential role for synaptopodin in activity-dependent regulation of dendritic spine dynamics and synaptic plasticity in postnatal brain development.
Deep counter networks for asynchronous event-based processing

Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models. We propose counter neurons as minimal spiking neuron models which only require addition and comparison operations, thus avoiding costly multiplications. We show how inference carried out in deep counter networks converges to the same accuracy levels as are achieved with state-of-the-art conventional networks. As their event-based style of computation leads to reduced latency and sparse updates, counter networks are ideally suited for efficient compact and low-power hardware implementation. We present theory and training methods for counter networks, and demonstrate on the MNIST benchmark that counter networks converge quickly, both in terms of time and number of operations required, to state-of-the-art classification accuracy.
Understanding the long-term spectral variability of Cygnus X-1 with BATSE and ASM observations

We present an analysis of all observations of Cygnus X-1 by the BATSE (20-300 keV) and by ASM (1.5-12 keV) until 2002 June, including 1200 days of simultaneous data. We find a number of correlations between fluxes and hardnesses in different energy bands. In the hard state, the variability can be explained by softening the overall spectrum with a pivot at 50 keV and another, independent variability pattern where the spectral shape does not change when the luminosity changes. In the soft state, the variability is caused by a variable hard tail of a constant shape superimposed on a constant soft component. These variability patterns are in agreement with the energy-dependent rms variability in the two states. We also study in detail recent soft states in 2000-02. The last of them has lasted so far for >200 days. Their spectra are harder in the 1.5-5 keV band but similar or in the 3-12 keV band than those of the 1996 soft state whereas the rms variability is stronger in all the ASM bands. On the other hand, the 1994 soft state transition observed by BATSE appears very similar to the 1996 one. We interpret the variability patterns by theoretical Comptonization models. In the hard state, the variability appears to be driven mostly by changing flux in seed photons Comptonized in a hot thermal plasma cloud with an approximately constant power supply. In the soft state, the variability is consistent with flares of hybrid, thermal/nonthermal, plasma with variable power above a stable cold disk. The spectral and timing differences between the 1996 and 2000-02 soft states are explained by a decrease of the color disk temperature. Also, based on broad-band pointed observations, we find the intrinsic bolometric luminosity increases by a factor of 3-4 from the hard state to the soft one.
miR-34a Repression in Proneural Malignant Gliomas Upregulates Expression of Its Target PDGFRA and Promotes Tumorigenesis

Glioblastoma (GBM) and other malignant gliomas are aggressive primary neoplasms of the brain that exhibit notable refractivity to standard treatment regimens. Recent large-scale molecular profiling has revealed distinct disease subclasses within malignant gliomas whose defining genomic features highlight dysregulated molecular networks as potential targets for therapeutic development. The “proneural” designation represents the largest and most heterogeneous of these subclasses, and includes both a large fraction of GBMs along with most of their lower-grade astrocytic and oligodendroglial counterparts. The pathogenesis of proneural gliomas has been repeatedly associated with dysregulated PDGF signaling. Nevertheless, genomic amplification or activating mutations involving the PDGF receptor (PDGFRA) characterize only a subset of proneural GBMs, while the mechanisms driving dysregulated PDGF signaling and downstream oncogenic networks in remaining tumors are unclear. MicroRNAs (miRNAs) are a class of small, noncoding RNAs that regulate gene expression by binding loosely complimentary sequences in target mRNAs. The role of miRNA biology in numerous cancer variants is well established. In an analysis of miRNA involvement in the phenotypic expression and regulation of oncogenic PDGF signaling, we found that miR-34a is downregulated by PDGF pathway activation in vitro. Similarly, analysis of data from the Cancer Genome Atlas (TCGA) revealed that miR-34a expression is significantly lower in proneural gliomas compared to other tumor subtypes. Using primary GBM cells maintained under neurosphere conditions, we then demonstrated that miR-34a specifically affects growth of proneural glioma cells in vitro and in vivo. Further bioinformatic analysis identified PDGFRA as a direct target of miR-34a and this interaction was experimentally validated. Finally, we found that PDGF-driven miR-34a repression is unlikely to operate solely through a p53-dependent mechanism. Taken together, our data support the existence of reciprocal negative feedback regulation involving miR-34 and PDGFRA expression in proneural gliomas and, as such, identify a subtype specific therapeutic potential for miR-34a.
Using fuzzy logic to recognize geometric shapes interactively

Presents a simple method, based on fuzzy logic, to recognize multi-stroke sketches of geometric shapes and uni-stroke gestural commands. It uses temporal adjacency and global geometric properties of figures to recognize a simple vocabulary of geometric shapes drawn in different line styles. The geometric features used (convex hull, largest-area inscribed and smallest-area enclosing polygons, perimeter and area ratios) are invariant with rotation and scale of figures. Through experimental evaluation we have found the method very usable with acceptable recognition rates although the multi-stroke approach poses problems in choosing appropriate values for timeouts. Although we have privileged simplicity over robustness, the method has proved suitable for interactive applications.
Hindsight Bias and Investment Performance

Once they have observed information, hindsight biased agents fail to remember how ignorant they were initially. This bias has been amply documented by the psychological literature. To study its economic consequences, we formulate a theoretical model of the hindsight bias, providing a foundation for previous empirical measures. We show theoretically that hindsight biased agents overreact to information signals, conduct incorrect learning about volatility, and engage in suboptimal investment decisions. To test empirically the hypothesis that the hindsight bias reduces performance in important economic situations, we collect psychometric and performance data from a sample of investment bankers in London and Frankfurt. We find strong evidence that these agents are hindsight biased, and that more biased agents have lower performance. These findings are robust to differences in location, information and experience. Many thanks for thoughtful comments to participants in the Toulouse and Amsterdam Universities seminars, the Warwick Conference on Behavioral Finance (2006) and the Munich Summer School on Economics and Psychology (2006), in particular Denis Hilton, Luis Garicano, Robin Hogarth, Florencio Lopez de Silanes, Thomas Mariotti, Paola sapienza, Jean Tirole and Luigi Zingales.
FTA and Export Platform Foreign Direct Investment: Evidence from Japanese firm level data

This paper examines the choice of foreign direct investment (FDI) among four types—traditional horizontal FDI, traditional vertical FDI, export-platform horizontal FDI, and export-platform vertical FDI—focusing in particular on the recent phenomena of the export-platform type FDI. The theoretical discussion shows a prediction of the effect of free trade agreements (FTAs) on the FDI type chosen. The empirical discussion provides descriptive statistics which point to the growing importance of export-platform type FDI. It then shows supportive evidence for the model's prediction, using Japan's firm-level FDI data. More specifically, it is shown that regional trade agreements (RTAs), such as the Association of Southeast Asian Nations (ASEAN) or the North America Free Trade Agreement (NAFTA), drives horizontal export-platform-type FDI, whereas bilateral FTAs (Japan's economic partnership agreement in the context of the data used in this paper) in some cases induce vertical export-platform type FDI. The findings suggest some policy implications for FDI recipient countries. First, the obvious positive effect of an RTA on horizontal export-platform type FDI is an encouraging finding for countries forming them in that it leads to a reduction in production costs and a concomitant rise in production/consumption. Even more importantly, the finding is a testament to a rarely mentioned benefit of smaller countries joining RTAs. Second, the positive effect of a bilateral FTA between Japan and Malaysia on the vertical export-platform type FDI is also reassuring in the same reason of cost reduction and production/consumption increase.
The Changing Frontier: Rethinking Science and Innovation Policy

In 1945, Vannevar Bush, founder of Raytheon and one-time engineering dean at MIT, delivered a report to the president of the United States that argued for the importance of public support for science, and the importance of science for the future of the nation. The report, Science: The Endless Frontier, set America on a path toward strong and well-funded institutions of science, creating an intellectual architecture that still defines scientific endeavor today. In The Changing Frontier, Adam B. Jaffe and Benjamin Jones bring together a group of prominent scholars to consider the changes in science and innovation in the ensuing decades. The contributors take on such topics as changes in the organization of scientific research, the geography of innovation, modes of entrepreneurship, and the structure of research institutions and linkages between science and innovation. An important analysis of where science stands today, The Changing Frontier will be invaluable to practitioners and policy makers alike.
The impact of college readiness programs on college continuation in Georgia

The purpose of this study was to analyze and explain the variance in patterns of college continuation rates for Georgia public high schools between the 1999 and 2005 academic years. I assessed the impact of college readiness programs on high school to college continuation rates in Georgia public high schools during the study period. I analyzed the variances as they relate to high schools’ participation or non-participation in one or more of seven popular college readiness programs. College continuation, as used in this study, refers to enrollment of a student in one of the 33 two-year or four-year institutions of the University System of Georgia (USG) by the fall semester of the year of high school graduation. Variances in patterns of college continuation for participating Georgia high schools may demonstrate, net of all other factors, the extent to which different programs are successful in their goal of assisting at-risk students’ transition to college. Transitioning to college is the first step towards the ultimate goal of helping participating students attain a college degree. 1 Currently there are 35 USG institutions. The Medical College of Georgia does not admit students directly from high school, hence it was excluded. The 35 college, Georgia Gwinnett College, admitted the first students in the Fall 2005; therefore, it is not included in this study. There were four primary sources of data for this study: Georgia Department of Education data on high schools, USG High School Feedback Reports, National Center of Education Statistics (NCES) Common Core Data reports (CCD) on characteristics of high schools, and the respective administrative sources of college readiness programs. The study yielded empirical data showing an association between participation in certain college readiness programs and college continuation rates in Georgia, net of all other explanatory factors such as school SES and geographic location. Furthermore, participation yielded different continuation rates for predominantly Black and White high schools, to different categories of University System of Georgia colleges and universities. Recommendations are made based on the findings and implications of the findings to policy and future research. INDEX WORDS: college readiness programs, college preparation programs, early intervention programs, college continuation. THE IMPACT OF COLLEGE READINESS PROGRAMS ON COLLEGE CONTINUATION IN GEORGIA: A LOGISTIC AND GLS REGRESSION ANALYSIS
Managing Joint Ventures

Executive Overview Joint ventures aid firms in accessing new markets, knowledge, capabilities, and other resources. Yet they can be challenging to manage, largely because they are owned by two or more parent companies. These companies may have competing or incongruent goals, differences in management style, and in the case of international business, additional complexities associated with differing government policies and business practices. We examine research on joint venture (JV) performance in order to identify prominent academic discussions established over the last 25 years. From this research, we draw implications from past research and areas for future research on successfully managing JVs, taking into account the decisions JV partners must make throughout the partnering process, from initial motivations through partner selection and negotiation of terms to implementation and ongoing management. Key implications include the necessity of honesty, trust, and commitment for the success of the JV, set.
Characterization of the Lipopolysaccharides and Capsules of Shewanella spp

ABSTRACT Electron microscopy, sodium dodecyl sulfate-polyacrylamide gel electrophoresis with silver staining and 1H, 13C, and 31P-nuclear magnetic resonance (NMR) were used to detect and characterize the lipopolysaccharides (LPSs) of several Shewanella species. Many expressed only rough LPS; however, approximately one-half produced smooth LPS (and/or capsular polysaccharides). Some LPSs were affected by growth temperature with increased chain length observed below 25°C. Maximum LPS heterogeneity was found at 15 to 20°C. Thin sections of freeze-substituted cells revealed that Shewanella oneidensis, S. algae, S. frigidimarina, and Shewanella sp. strain MR-4 possessed either O-side chains or capsular fringes ranging from 20 to 130 nm in thickness depending on the species. NMR detected unusual sugars in S. putrefaciens CN32 and S. algae BrYDL. It is possible that the ability of Shewanella to adhere to solid mineral phases (such as iron oxides) could be affected by the composition and length of surface polysaccharide polymers. These same polymers in S. algae may also contribute to this opportunistic pathogen's ability to promote infection.
Generalization in a linear perceptron in the presence of noise

The authors study the evolution of the generalization ability of a simple linear perceptron with N inputs which learns to imitate a 'teacher perceptron'. The system is trained on p= alpha N example inputs drawn from some distribution and the generalization ability is measured by the average agreement with the teacher on test examples drawn from the same distribution. The dynamics may be solved analytically and exhibits a phase transition from imperfect to perfect generalization at alpha =1, when there are no errors (static noise) in the training examples. If the examples are produced by an erroneous teacher, overfitting is observed, i.e. the generalization error starts to increase after a finite time of training. It is shown that a weight decay of the same size as the variance of the noise (errors) on the teacher improves on the generalization and suppresses the overfitting. The generalization error as a function of time is calculated numerically for various values of the parameters. Finally dynamic noise in the training is considered. White noise on the input corresponds on average to a weight decay, and can thus improve generalization, whereas white noise on the weights or the output degrades generalization. Generalization is particularly sensitive to noise on the weights (for alpha (1) where it makes the error constantly increase with time, but this effect is also shown to be damped by a weight decay. Weight noise and output noise acts similarly above the transition at alpha =1.
Designing relevant features for visual speech recognition

Automatic speech analysis is currently evolving towards hybrid systems that combine both visual and acoustic information. This is due to limitations of existing acoustic-based approaches and the need for robust speech recognition systems working under extremely challenging conditions including noisy environments. We introduce in this paper a novel visual speech recognition approach, based on string kernels and support vector machines. The main contributions of this work include (i) the design of a similarity function, based on string kernels, that models the dynamics as well as the appearance of visual features in talking faces and (ii) a kernel combination procedure based on multiple kernel learning, that makes visual feature selection effective and also more tractable. Experiments conducted, on a standard digit database, show that the proposed algorithm outperforms current state-of-the-art methods.