Miracle-dz/UniFAR-FTUs huggingface dataset:
Luminosity-dependent Quasar Lifetimes: Reconciling the Optical and X-Ray Quasar Luminosity Functions

We consider implications of our new model of quasar lifetimes and light curves for the quasar luminosity function (LF) at different frequencies and redshifts. In our picture, quasars evolve rapidly and the lifetime depends on both their instantaneous and peak luminosities. The bright end of the LF traces the peak intrinsic quasar activity, but the faint end consists of quasars that are either undergoing exponential growth to much larger masses and luminosities or are in sub-Eddington quiescent states going into or coming out of a period of peak activity. The "break" in the observed LF corresponds directly to the maximum in the intrinsic distribution of peak luminosities, which falls off at both brighter and fainter luminosities. We study this model using simulations of galaxy mergers that successfully reproduce a wide range of observed quasar phenomena, including the observed column density distribution. By combining quasar lifetimes and the distribution of maximum quasar luminosities determined from the observed hard X-ray LF with the corresponding luminosity and host system-dependent column densities, we produce the expected soft X-ray and B-band LFs. Our predictions agree exceptionally well with the observed LFs at all observed luminosities over the redshift range considered (z ≤ 1) without invoking any ad hoc assumptions about an obscured population of sources. Our results also suggest that observed correlations in hard X-ray samples between the obscured fraction of quasars and luminosity can be explained in the context of our model by the expulsion of surrounding gas due to heating from accretion feedback energy as a quasar nears its peak luminosity and final black hole mass.
Killing Two Birds with One Stone: Natural Rice Rhizospheric Microbes Reduce Arsenic Uptake and Blast Infections in Rice

Our recent work has shown that a rice thizospheric natural isolate, a Pantoea sp (hereafter EA106) attenuates Arsenic (As) uptake in rice. In parallel, yet another natural rice rhizospheric isolate, a Pseudomonas chlororaphis (hereafter EA105), was shown to inhibit rice blast pathogen Magnaporthe oryzae. Considering the above, we envisaged to evaluate the importance of mixed stress regime in rice plants subjected to both As toxicity and blast infections. Plants subjected to As regime showed increased susceptibility to blast infections compared to As-untreated plants. Rice blast pathogen M. oryzae showed significant resistance against As toxicity compared to other non-host fungal pathogens. Interestingly, plants treated with EA106 showed reduced susceptibility against blast infections in plants pre-treated with As. This data also corresponded with lower As uptake in plants primed with EA106. In addition, we also evaluated the expression of defense related genes in host plants subjected to As treatment. The data showed that plants primed with EA106 upregulated defense-related genes with or without As treatment. The data shows the first evidence of how rice plants cope with mixed stress regimes. Our work highlights the importance of natural association of plant microbiome which determines the efficacy of benign microbes to promote the development of beneficial traits in plants.
Clustering and compositionality of task representations in a neural network trained to perform many cognitive tasks

A neural system has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained a single network model to perform 20 cognitive tasks that may involve working memory, decision-making, categorization and inhibitory control. We found that after training, recurrent units developed into clusters that are functionally specialized for various cognitive processes. We introduce a measure to quantify relationships between single-unit neural representations of tasks, and report five distinct types of such relationships that can be tested experimentally. Surprisingly, our network developed compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, we demonstrate how the network could learn multiple tasks sequentially. This work provides a computational platform to investigate neural representations of many cognitive tasks.
μC-States: Fine-grained GPU datapath power management

To improve the performance of Graphics Processing Units (GPUs) beyond simply increasing core count, architects are recently adopting a scale-up approach: the peak throughput and individual capabilities of the GPU cores are increasing rapidly. This big-core trend in GPUs leads to various challenges, including higher static power consumption and lower and imbalanced utilization of the datapath components of a big core. As we show in this paper, two key problems ensue: (1) the lower and imbalanced datapath utilization can waste power as an application does not always utilize all portions of the big core datapath, and (2) the use of big cores can lead to application performance degradation in some cases due to the higher memory system contention caused by the more memory requests generated by each big core. This paper introduces a new analysis of datapath component utilization in big-core GPUs based on queuing theory principles. Building on this analysis, we introduce a fine-grained dynamic power- and clock-gating mechanism for the entire datapath, called μC-States, which aims to minimize power consumption by turning off or tuning-down datapath components that are not bottlenecks for the performance of the running application. Our experimental evaluation demonstrates that μC-States significantly reduces both static and dynamic power consumption in a big-core GPU, while also significantly improving the performance of applications affected by high memory system contention. We also show that our analysis of datapath component utilization can guide scheduling and design decisions in a GPU architecture that contains heterogeneous cores.
The retrotrapezoid nucleus neurons expressing Atoh1 and Phox2b are essential for the respiratory response to CO2

Maintaining constant CO2 and H+ concentrations in the arterial blood is critical for life. The principal mechanism through which this is achieved in mammals is the respiratory chemoreflex whose circuitry is still elusive. A candidate element of this circuitry is the retrotrapezoid nucleus (RTN), a collection of neurons at the ventral medullary surface that are activated by increased CO2 or low pH and project to the respiratory rhythm generator. Here, we use intersectional genetic strategies to lesion the RTN neurons defined by Atoh1 and Phox2b expression and to block or activate their synaptic output. Photostimulation of these neurons entrains the respiratory rhythm. Conversely, abrogating expression of Atoh1 or Phox2b or glutamatergic transmission in these cells curtails the phrenic nerve response to low pH in embryonic preparations and abolishes the respiratory chemoreflex in behaving animals. Thus, the RTN neurons expressing Atoh1 and Phox2b are a necessary component of the chemoreflex circuitry. DOI: http://dx.doi. org/10.7554/eLife.07051. 001
A framework for inferring predictive distributions of rhino poaching events through causal modelling

Rhino poaching in South Africa is leading to a catastrophic reduction in the rhino population. In this paper a Bayesian network causal model is proposed to model the underlying (causal) relationships that lead to rhino poaching events. The model may be used to fuse a collection of heterogeneous information sources. If a game reserve is partitioned into several geographical areas or cells, the model may perform inference for each of these cells separately, and give a relative predictive distribution of poaching events over the game reserve. After an overview of the current problem definition and a brief overview of similar modelling approaches, the Bayesian network model is presented. The developed Bayesian network based model is an initial attempt at proposing a sensible modelling approach for this problem. Some of the complexities of the approach are discussed, before considering how the model may be validated at a later stage.
Effects of a flaxseed-derived lignan supplement on C-reactive protein, IL-6 and retinol-binding protein 4 in type 2 diabetic patients

Elevated C-reactive protein (CRP), IL-6 and retinol-binding protein 4 (RBP4) levels are associated with insulin resistance and diabetes mellitus. Phytoestrogens (including lignans and isoflavones) may enhance the management of diabetes and are hypothesized to act through inflammation pathways. The present study explored the effects of flaxseed-derived lignan on inflammatory factors and RBP4 concentrations in type 2 diabetics, who have higher levels of these biomarkers. Seventy community-dwelling diabetic patients (twenty-six men and forty-four post-menopausal women) with mild hypercholesterolaemia completed a randomized, double-blind, placebo-controlled, cross-over trial of supplementation with flaxseed-derived lignan capsules (360 mg/d) or placebo for 12 weeks, separated by an 8-week wash-out period. The participants maintained their habitual diets and levels of physical activity. Baseline to follow-up concentrations of CRP increased significantly within the placebo group (1·42 (sem 0·19) v. 1·96 (sem 0·22) mg/l, P < 0·001), but were comparatively unchanged in the lignan-supplemented group (1·67 (sem 0·19) v. 1·90 (sem 0·26) mg/l, P = 0·94); a significant difference was observed between treatments ( − 0·45 (95 % CI − 0·76, − 0·08) mg/l, P = 0·021). This effect was confined to women (P = 0·016), but not observed in men (P = 0·49). No between-treatment differences were found with regard to IL-6 or RBP4; though IL-6 concentrations increased significantly from baseline to follow-up in both groups (P = 0·004 and P < 0·001 following lignan and placebo treatments, respectively). The study suggests that lignan might modulate CRP levels in type 2 diabetics. These results need to be confirmed by further large clinical trials of longer duration.
Valuation of a CDO and an n-th to Default CDS Without Monte Carlo Simulation

Many of the new credit derivative products are based on default experience for a portfolio of financial instruments. These include collateralized debt obligations (CDOs) and similar tranched credit products, and “n-th to default swaps. ” Devising good default risk models for single-name credits has been challenging enough, but applying them to credit portfolios introduces much greater complexity, because of the critical importance of correlation. The most common valuation technology is Monte Carlo simulation, but with many bonds, each of which is subject to both correlated and idiosyncratic risk factors, the simulation is time-consuming and limited in scope. In this article, Hull and White offer two straightforward approximation techniques for evaluating default risk within the industry-standard “copula“ model that eliminate simulation of the idiosyncratic risks. Their approach greatly accelerates the solution while still allowing a large degree of flexibility in the choice of factor correlation structure and probability distributions. For example, Student-t distributed shocks that have fatter tails than the normal are easily accommodated.
Evolution of Bit Strings II: A Simple Model of Co-Evolution

In this paper we present the results of simple co-evolution ary models that simulate the temporal development of a population of interacting bit strings. Each bit string is decoded into functional groups called "genes. " The individuals participate in a procedure similar to the Darwinian principle, that is, random interaction (in which the genes determine the fitness of an individual) followed by deterministic selection including random errors during reproduction. We use three selection models: selection by sorting and replacing, tournament selection, and energy flow. We compare the results for each model to those of a random selection model to ensure that "emergent" (non-trivial) features are really caused by evolutionary processes. The influence of selection is clearly evident when comparing the age distributions of individu als and species to those of the random-selection case. While the age distribution of the individuals shows a systematic deviation from an exponential behavior in the selection case, the species ages tend to follow a power law when applying non-random selection. The species show a dynamic of "punctuated equilibria" that is similar to the be havior of natural systems.
Sparsity induced similarity measure for label propagation

Graph-based semi-supervised learning has gained considerable interests in the past several years thanks to its effectiveness in combining labeled and unlabeled data through label propagation for better object modeling and classification. A critical issue in constructing a graph is the weight assignment where the weight of an edge specifies the similarity between two data points. In this paper, we present a novel technique to measure the similarities among data points by decomposing each data point as an L1 sparse linear combination of the rest of the data points. The main idea is that the coefficients in such a sparse decomposition reflect the point's neighborhood structure thus providing better similarity measures among the decomposed data point and the rest of the data points. The proposed approach is evaluated on four commonly-used data sets and the experimental results show that the proposed Sparsity Induced Similarity (SIS) measure significantly improves label propagation performance. As an application of the SIS-based label propagation, we show that the SIS measure can be used to improve the Bag-of-Words approach for scene classification.
The Mutual Information of a MIMO Channel : A Survey

In this paper, we survey recent mathematical results devoted to the study of the mutual information of MIMO channels in the case where transmit and receive antennas converge to∞ at the same rate. We express the different results in a unified framework and the emphasis is put on non-asymptotic deterministic approximations of the mutual information, asymptotic limits (when existing) and Ricean correlated channels.
A study of practical deduplication

We collected file system content data from 857 desktop computers at Microsoft over a span of 4 weeks. We analyzed the data to determine the relative efficacy of data deduplication, particularly considering whole-file versus block-level elimination of redundancy. We found that whole-file deduplication achieves about three quarters of the space savings of the most aggressive block-level deduplication for storage of live file systems, and 87% of the savings for backup images. We also studied file fragmentation, finding that it is not prevalent, and updated prior file system metadata studies, finding that the distribution of file sizes continues to skew toward very large unstructured files.
Memory Transformation Enhances Reinforcement Learning in Dynamic Environments

Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as “memory transformation. ” Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales. SIGNIFICANCE STATEMENT As time passes, memories transform from a highly detailed state to a more gist-like state, in a process called “memory transformation. ” Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales.
Rank Aggregation Revisited

The rank aggregation problem is to combine many different rank orderings on the same set of candidates, or alternatives, in order to obtain a “better” ordering. Rank aggregation has been studied extensively in the context of social choice theory, where several “voting paradoxes” have been discovered. The problem also arises in many other settings: Sports and Competition: How to determine the winner of a season, how to rank players or how to compare players from different eras? Machine Learning: Collaborative filtering and meta-search; Statistics: Notions of Correlation; Database Middleware: Combining results from multiple databases. A natural step toward aggregation was taken by Kemeny. Informally, given orderings k on (partial lists of) alternatives f ng, a Kemeny optimal ordering minimizes the sum of the “bubble sort” distances ki K i Thus, intuitively, Kemeny optimal solutions produce “best” compromise orderings. However, finding a Kemeny optimal aggregation is NP-hard [4]. In this work we revisit rank aggregation with an eye toward reducing search engine spam in metasearch. We note the virtues of Kemeny optimal aggregation in this context, strengthen the NP-hardness results, and, most importantly, develop a natural relaxation called local Kemeny optimality that preserves the spam-fighting capabilities of Kemeny optimality at vastly reduced cost. We show how to efficiently take any initial aggregated ordering and produce a maximally consistent locally Kemeny optimal solution. We therefore propose a new approach to rank aggregation: begin with any desirable initial aggregation and then “locally Kemenize” it. We also propose the use of Markov chains for obtaining the initial aggregation, and suggest four specific chains for this purpose. Compaq Systems Research Center, 130 Lytton Ave., Palo Alto, CA 94301. dwork@pa.dec.com IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120. ravi@almaden.ibm. com Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel. Part of this work was done while the author was visiting the IBM Almaden Research Center. naor@wisdom.weizmann. ac.il IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120. siva@almaden.ibm. com
THERMAL ACCLIMATION, NEUROMUSCULAR SYNAPTIC DELAY AND MINIATURE END-PLATE CURRENT DECAY IN THE FROG RANA TEMPORARIA

1. The effects of 1 or 2­3 weeks of acclimation to 4 °C and 24 °C of overwintering grass frogs (Rana temporaria) on the synaptic delay and on the time constant of the decay phase (tau) of miniature end-plate currents (MEPCs) in the neuromuscular junction of sartorius muscle were studied. In order to equalize the possible effects of differential starvation, the animals were usually cross-acclimated to the two temperatures. 2. Synaptic delay was not affected by temperature acclimation but was slightly prolonged by the more profound starvation at the higher temperature when the cross-acclimation procedure was not used. The average Q10 of synaptic delay between 4 and 24 °C was 2.60 and of minimum synaptic delays, 2.64. The corresponding values for apparent activation energy (Ea) were 65.79 and 66.48 kJ mol-1. 3. The time constant of the decay phase of MEPCs was not affected by temperature acclimation. The average Q10 between 4 and 24 °C was 2.27. The corresponding Ea value was 56.02 kJ mol-1. 4. The function of peripheral neuromuscular synapses is well regulated and changes in its time relationships do not appear to be involved in the thermal acclimation of frogs.
Identifying Human Kinase-Specific Protein Phosphorylation Sites by Integrating Heterogeneous Information from Various Sources

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.
Intergenerational correlation of domestic work: Does gender matter?

Despite the increasing prevalence of dual-earner couples, women still perform the bulk of domestic and parental tasks within the household. In this paper, we investigate the role of the parental model in the persistence of this gender inequality. We study the possible correlation between the domestic time of parents and their young adult co-resident children using the French time-use survey conducted in 1999-2000 in which all family members aged above 14 years old were interviewed. Estimation results show a positive relationship between child and parental housework times. Girls' participation in domestic tasks is much higher than that of boys, but a gendered effect of the intergenerational relationship is not systematically confirmed and depends on the type of domestic tasks.
D-Branes and Scheme Theory

Tom´as G´omezTata Institute for Fundamental ResearchHomi Bhabha Road, 400 005 Mumbai (India)tomas@math.tifr. res.inEric SharpeDepartment of PhysicsBox 90305Duke UniversityDurham, NC 27708ersharpe@cgtp.duke. eduIn this highly speculative note we conjecture that it may be possible to understandsome features of coincident D-branes, such as the appearance of enhanced non-abelian gaugesymmetry, in a purely geometric fashion, using a form of geometry known as scheme theory.We give a very brief introduction to some relevant ideas from scheme theory, and point outhow these ideas work in special cases.August 20001