cssi/SciSciGPT-SciSciNet huggingface dataset:
Spatio-temporal dynamics of turbulent separation bubbles

The spatio-temporal dynamics of separation bubbles induced to form in a fully developed turbulent boundary layer (with Reynolds number based on momentum thickness of the boundary layer of 490) over a flat plate is studied via direct numerical simulations. Two different separation bubbles are examined: one induced by a suction–blowing velocity profile on the top boundary and the other by a suction-only velocity profile. The latter condition allows reattachment to occur without an externally imposed favourable pressure gradient and leads to a separation bubble more representative of those occurring over airfoils and in diffusers. The suction-only separation bubble exhibits a range of clearly distinguishable modes, including a high-frequency mode and a low-frequency ‘breathing’ mode that has been observed in some previous experiments. The high-frequency mode is well characterized by classical frequency scalings for a plane mixing layer and is associated with the formation and shedding of spanwise-oriented vortex rollers. The topology associated with the low-frequency motion is revealed by applying dynamic mode decomposition to the data from the simulations and is shown to be dominated by highly elongated structures in the streamwise direction. The possibility of Gortler instability induced by the streamwise curvature on the upstream end of the separation bubble as the underlying mechanism for these structures and the associated low frequency is explored.
Inhibition of the Dead Box RNA Helicase 3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

HIV-1 Associated Neurocognitive Disorder (HAND) is a common and clinically detrimental complication of HIV infection. Viral proteins, including Tat, released from infected cells, cause neuronal toxicity. Substance abuse in HIV-infected patients greatly influences the severity of neuronal damage. To repurpose small molecule inhibitors for anti-HAND therapy, we employed MOLIERE, an AI-based literature mining system that we developed. All human genes were analyzed and prioritized by MOLIERE to find previously unknown targets connected to HAND. From the identified high priority genes, we narrowed the list to those with known small molecule ligands developed for other applications and lacking systemic toxicity in animal models. To validate the AI-based process, the selective small molecule inhibitor of DDX3 helicase activity, RK-33, was chosen and tested for neuroprotective activity. The compound, previously developed for cancer treatment, was tested for the prevention of combined neurotoxicity of HIV Tat and cocaine. Rodent cortical cultures were treated with 6 or 60 ng/ml of HIV Tat and 10 or 25 μM of cocaine, which caused substantial toxicity. RK-33 at doses as low as 1 μM greatly reduced the neurotoxicity of Tat and cocaine. Transcriptome analysis showed that most Tat-activated transcripts are microglia-specific genes and that RK-33 blocks their activation. Treatment with RK-33 inhibits the Tat and cocaine-dependent increase in the number and size of microglia and the proinflammatory cytokines IL-6, TNF-α, MCP-1/CCL2, MIP-2, IL-1α and IL-1β. These findings reveal that inhibition of DDX3 may have the potential to treat not only HAND but other neurodegenerative diseases.
Pattern Avoidance by Even Permutations

We study questions of even-Wilf-equivalence, the analogue of Wilf-equivalence when attention is restricted to pattern avoidance by permutations in the alternating group. Although some Wilf-equivalence results break when considering even-Wilf-equivalence analogues, we prove that other Wilf-equivalence results continue to hold in the even-Wilf-equivalence setting. In particular, we prove that $t(t-1)\cdots 321$ and $(t-1)(t-2)\cdots 21t$ are even-shape-Wilf-equivalent for odd $t$, paralleling a result (which held for all $t$) of Backelin, West, and Xin for shape-Wilf-equivalence. This allows us to classify the symmetric group $\mathcal{S}_{4}$, and to partially classify $\mathcal{S}_{5}$ and $\mathcal{S}_{6}$, according to even-Wilf-equivalence. As with transition to involution-Wilf-equivalence, some—but not all—of the classical Wilf-equivalence results are preserved when we make the transition to even-Wilf-equivalence.
Implications of a class of grand-unified theories for large-scale structure in the universe

A class of grand-unified theories in which cosmologically significant axion and neutrino energy densities arise naturally is considered. To obtain large-scale structure, attention is given to (1) an inflationary scenario, (2) inflation followed by string production, and (3) a noninflationary scenario with density fluctuations caused solely by strings. It is shown that inflation may be compatible with the recent observational indications that Omega less than 1 on the scale of superclusters, particularly if strings are present.
Cortical auditory distance representation based on direct-to-reverberant energy ratio.

Abstract Auditory distance perception and its neuronal mechanisms are poorly understood, mainly because 1) it is difficult to separate distance processing from intensity processing, 2) multiple intensity-independent distance cues are often available, and 3) the cues are combined in a context-dependent way. A recent fMRI study identified human auditory cortical area representing intensity-independent distance for sources presented along the interaural axis (Kopco et al. PNAS, 109, 11019-11024). For these sources, two intensity-independent cues are available, interaural level difference (ILD) and direct-to-reverberant energy ratio (DRR). Thus, the observed activations may have been contributed by not only distance-related, but also direction-encoding neuron populations sensitive to ILD. Here, the paradigm from the previous study was used to examine DRR-based distance representation for sounds originating in front of the listener, where ILD is not available. In a virtual environment, we performed behavioral and fMRI experiments, combined with computational analyses to identify the neural representation of distance based on DRR. The stimuli varied in distance (15–100 ​cm) while their received intensity was varied randomly and independently of distance. Behavioral performance showed that intensity-independent distance discrimination is accurate for frontal stimuli, even though it is worse than for lateral stimuli. fMRI activations for sounds varying in frontal distance, as compared to varying only in intensity, increased bilaterally in the posterior banks of Heschl’s gyri, the planum temporale, and posterior superior temporal gyrus regions. Taken together, these results suggest that posterior human auditory cortex areas contain neuron populations that are sensitive to distance independent of intensity and of binaural cues relevant for directional hearing.
Dynamic reorganization of the frontal parietal network during cognitive control and episodic memory

Higher cognitive functioning is supported by adaptive reconfiguration of large-scale functional brain networks. Cognitive control (CC), which plays a vital role in flexibly guiding cognition and behavior in accordance with our goals, supports a range of executive functions via distributed brain networks. These networks process information dynamically and can be represented as functional connectivity changes between network elements. Using graph theory, we explored context-dependent network reorganization in 56 healthy adults performing fMRI tasks from two cognitive domains that varied in CC and episodic-memory demands. We examined whole-brain modular structure during the DPX task, which engages proactive CC in the frontal-parietal cognitive-control network (FPN), and the RiSE task, which manipulates CC demands at encoding and retrieval during episodic-memory processing, and engages FPN, the medial-temporal lobe and other memory-related networks in a context dependent manner. Analyses revealed different levels of network integration and segregation. Modularity analyses revealed greater brain-wide integration across tasks in high CC conditions compared to low CC conditions. Greater network reorganization occurred in the RiSE memory task, which is thought to require coordination across multiple brain networks, than in the DPX cognitive-control task. Finally, FPN, ventral attention, and visual systems showed within network connectivity effects of cognitive control; however, these cognitive systems displayed varying levels of network reorganization. These findings provide insight into how brain networks reorganize to support differing task contexts, suggesting that the FPN flexibly segregates during focused proactive control and integrates to support control in other domains such as episodic memory.
Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be "mutually-exclusive", such that no single model can solve all of the tasks at once. For example when creating tasks for a meta-learned N-way image classifier, we typically randomize the assignment of the image classes to N-way classification labels for each task. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing an information-theoretic meta-regularization objective that places precedence on data-driven adaptation. This causes the meta-learner to decide what should be learned from data and what must be inferred from the input. By doing so, our algorithm can successfully use data from "non-mutually-exclusive" tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where standard meta-learning has been difficult to apply. Our approach substantially outperforms standard meta-learning algorithms in these settings.
Efficient band gap prediction for solids.

An efficient method for the prediction of fundamental band gaps in solids using density functional theory (DFT) is proposed. Generalizing the Delta self-consistent-field ($\ensuremath{\Delta}\mathrm{SCF}$) method to infinite solids, the $\ensuremath{\Delta}$-sol method is based on total-energy differences and derived from dielectric screening properties of electrons. Using local and semilocal exchange-correlation functionals (local density and generalized gradient approximations), we demonstrate a 70% reduction of mean absolute errors compared to Kohn-Sham gaps on over 100 compounds with experimental gaps of 0.5--4 eV, at computational costs similar to typical DFT calculations.
Maternal Experience-Dependent Cortical Plasticity in Mice Is Circuit- and Stimulus-Specific and Requires MECP2.

The neurodevelopmental disorder Rett syndrome is caused by mutations in the gene Mecp2 Misexpression of the protein MECP2 is thought to contribute to neuropathology by causing dysregulation of plasticity. Female heterozygous Mecp2 mutants (Mecp2het ) failed to acquire a learned maternal retrieval behavior when exposed to pups, an effect linked to disruption of parvalbumin-expressing inhibitory interneurons (PV) in the auditory cortex. Nevertheless, how dysregulated PV networks affect the neural activity dynamics that underlie auditory cortical plasticity during early maternal experience is unknown. Here we show that maternal experience in WT adult female mice (WT) triggers suppression of PV auditory responses. We also observe concomitant disinhibition of auditory responses in deep-layer pyramidal neurons that is selective for behaviorally relevant pup vocalizations. These neurons further exhibit sharpened tuning for pup vocalizations following maternal experience. All of these neuronal changes are abolished in Mecp2het , suggesting that they are an essential component of maternal learning. This is further supported by our finding that genetic manipulation of GABAergic networks that restores accurate retrieval behavior in Mecp2het also restores maternal experience-dependent plasticity of PV. Our data are consistent with a growing body of evidence that cortical networks are particularly vulnerable to mutations of Mecp2 in PV neurons. Moreover, our work links, for the first time, impaired in vivo cortical plasticity in awake Mecp2 mutant animals to a natural, ethologically relevant behavior.SIGNIFICANCE STATEMENT Rett syndrome is a genetic disorder that includes language communication problems. Nearly all Rett syndrome is caused by mutations in the gene that produces the protein MECP2, which is important for changes in brain connectivity believed to underlie learning. We previously showed that female Mecp2 mutants fail to learn a simple maternal care behavior performed in response to their pups' distress cries. This impairment appeared to critically involve inhibitory neurons in the auditory cortex called parvalbumin neurons. Here we record from these neurons before and after maternal experience, and we show that they adapt their response to pup calls during maternal learning in nonmutants, but not in mutants. This adaptation is partially restored by a manipulation that improves learning.
Boosting gene expression clustering with system-wide biological information: a robust autoencoder approach

One of the first computational steps in exploration and analysis of the gene expression data is clustering. However, most of the standard clustering methods do not take prior biological information into account. Here, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencode, and from incorporating prior system-wide biological information into the clustering process. We tested our approach on two gene expression datasets. Our approach outperformed all other clustering methods on the labelled yeast gene expression dataset. Furthermore, we showed that it is better in identifying the functionally common clusters on the unlabelled human gene expression dataset. The results demonstrate that our new deep learning architecture can generalise well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge is helpful in the gene expression clustering.
Hybrid Binary-Unary Hardware Accelerator

Stream-based computing such as stochastic computing has been used in recent years to create designs with significantly smaller area by harnessing unary encoding of data. However, the area saving comes at an exponential price in latency, making the area × delay cost unattractive. In this article, we present a novel method which uses a hybrid binary / unary representation to perform computations. We first divide the input range into a few sub-regions, perform unary computations on each sub-region individually, and finally pack the outputs of all sub-regions back to compact binary. Moreover, we propose a synthesis methodology and a regression model to predict an optimal or close-to-optimal design in the design space. To the best of our knowledge, we are the first to show a scalable method based on parallel bit-stream data representation that can beat conventional binary in terms of a real cost, i.e., area × delay and energy consumption in almost all functions that we tried at resolutions of 8-, 10-, and 12-bits. Our method outperforms the binary, stochastic, and fully unary methods on a number of functions, especially low-cost binary CORDIC-based functions, and on a common edge detection algorithm on FPGA and in ASIC implementation. In terms of area × delay cost, our {on FPGA, in ASIC} cost is on average only { $4.72\%$ 4 . 72 % , $24.36\%$ 24 . 36 % } and { $20.16\%$ 20 . 16 % , $60.12\%$ 60 . 12 % } of the parallel binary pipeline implementation at 8- and 10-bit resolution, respectively. These numbers are 2–3 orders of magnitude better than the results of traditional stochastic methods. Our method is not competitive with the parallel CORDIC-based pipeline binary method for high-resolution (12-bit), highly oscillating functions such as $\sin (15 x)$ sin ( 15 x ) . However, for complex functions like $gamma$ g a m m a function, the proposed method can beat any other methods in terms of area × delay, throughput, latency, and energy per sample costs. To implement the Roberts cross edge detection algorithm, the proposed method takes 5.7 and 39.45 percent of the area × delay cost of FPGA and ASIC implementation of the binary method, respectively. In terms of energy efficiency for FPGA implementation, our method uses only 8.4, 12.7, and 27.7 percent of the energy per sample usage of serial binary implementations at 8-, 10-, and 12-bit resolutions, respectively. These numbers change to 23.9, 38.54, and 99.3 percent compared to parallel binary implementations.
Theoretical framework to surpass the Betz limit using unsteady fluid mechanics

The Betz limit expresses the maximum proportion of the kinetic energy flux incident on an energy conversion device that can be extracted from an unbounded flow. The derivation of the Betz limit requires an assumption of steady flow through a notional actuator disk that is stationary in the streamwise direction. The present derivation relaxes the assumptions of steady flow and streamwise actuator disk stationarity, which expands the physically realizable parameter space of flow conditions upstream and downstream of the actuator disk. A key consequence of this generalization is the existence of unsteady motions that can, in principle, lead to energy conversion efficiencies that exceed the Betz limit not only transiently but also in time-averaged performance. Potential physical implementations of those unsteady motions are speculated.
Studying rare events using forward-flux sampling: Recent breakthroughs and future outlook

Rare events are processes that occur upon the emergence of unlikely fluctuations. Unlike what their name suggests, rare events are fairly ubiquitous in nature, as the occurrence of many structural transformations in biology and material sciences is predicated upon crossing large free energy barriers. Probing the kinetics and uncovering the molecular mechanisms of possible barrier crossings in a system is critical to predicting and controlling its structural and functional properties. Due to their activated nature, however, rare events are exceptionally difficult to study using conventional experimental and computational techniques. In recent decades, a wide variety of specialized computational techniques-known as advanced sampling techniques-have been developed to systematically capture improbable fluctuations relevant to rare events. In this perspective, we focus on a technique called forward flux sampling [Allen et al., J. Chem. Phys. 124, 024102 (2006)] and overview its recent methodological variants and extensions. We also provide a detailed overview of its application to study a wide variety of rare events and map out potential avenues for further explorations.
Exposition and Interpretation of the Topology of Neural Networks

Convolutional neural networks (CNN's) are powerful and widely used tools. However, their interpretability is far from ideal. One such shortcoming is the difficulty of deducing a network's ability to generalize to unseen data. We use topological data analysis to show that the information encoded in the weights of a CNN can be organized in terms of a topological data model and demonstrate how such information can be interpreted and utilized. We show that the weights of convolutional layers at depths from 1 through 13 learn simple global structures. We also demonstrate the change of the simple structures over the course of training. In particular, we define and analyze the spaces of spatial filters of convolutional layers and show the recurrence, among all networks, depths, and during training, of a simple circle consisting of rotating edges, as well as a less recurring unanticipated complex circle that combines lines, edges, and non-linear patterns. We also demonstrate that topological structure correlates with a network's ability to generalize to unseen data and that topological information can be used to improve a network's performance. We train over a thousand CNN's on MNIST, CIFAR-10, SVHN, and ImageNet.
Chance and necessity in the pleiotropic consequences of adaptation for budding yeast

Mutations that a population accumulates during evolution in one 'home' environment may cause fitness gains or losses in other environments. Such pleiotropic fitness effects determine the evolutionary fate of the population in variable environments and can lead to ecological specialization. It is unclear how the pleiotropic outcomes of evolution are shaped by the intrinsic randomness of the evolutionary process and by the deterministic variation in selection pressures across environments. Here, to address this question, we evolved 20 replicate populations of the yeast Saccharomyces cerevisiae in 11 laboratory environments and measured their fitness across multiple conditions. We found that evolution led to diverse pleiotropic fitness gains and losses, driven by multiple types of mutations. Approximately 60% of this variation is explained by the home environment of a clone and the most common parallel genetic changes, whereas about 40% is attributed to the stochastic accumulation of mutations whose pleiotropic effects are unpredictable. Although populations are typically specialized to their home environment, generalists also evolved in almost all of the conditions. Our results suggest that the mutations that accumulate during evolution incur a variety of pleiotropic costs and benefits with different probabilities. Thus, whether a population evolves towards a specialist or a generalist phenotype is heavily influenced by chance.
Atomicity Checking in Linear Time using Vector Clocks

Multi-threaded programs are challenging to write. Developers often need to reason about a prohibitively large number of thread interleavings to reason about the behavior of software. A non-interference property like atomicity can reduce this interleaving space by ensuring that any execution is equivalent to an execution where all atomic blocks are executed serially. We consider the well studied notion of conflict serializability for dynamically checking atomicity. Existing algorithms detect violations of conflict serializability by detecting cycles in a graph of transactions observed in a given execution. The number of edges in such a graph can grow quadratically with the length of the trace making the analysis not scalable. In this paper, we present AeroDrome, a novel single pass linear time algorithm that uses vector clocks to detect violations of conflict serializability in an online setting. Experiments show that AeroDrome scales to traces with a large number of events with significant speedup.
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access

The uncoordinated spectrum access problem is studied using a multi-player multi-armed bandits framework. In the considered system model there is no central control and the users cannot communicate with each other. Furthermore, the environment may appear differently to different users, i.e. , the mean rewards as seen by different users for a particular channel may be different. With this setup, we present a policy that achieves expected regret of O (log T ) over a time horizon of duration T.
Antitrust's "Curse of Bigness" Problem

Tim Wu’s most recent book, The Curse of Bigness: Antitrust in the Gilded Age, is an attempt to reframe contemporary antitrust debates by returning antitrust to its more populist roots. Given the global implications of his ideas and policy proposals (including breakup of tech platforms) for many of the large corporations that he takes on, The Curse of Bigness offers profound insights for how society and business should be organized. The first part of this Review summarizes Wu’s major claims. It then highlights some of his critiques as to “bigness,” the multiple goals of antitrust, and the missed opportunities as to cases that should have been or need to be brought, such as against tech companies. Some of Wu’s critiques are spot on in identifying missed opportunities, like a number of horizontal mergers that should have been challenged. Where Wu’s book suffers is where he undervalues the institutional structure of antitrust law, underplays what antitrust does well as a substantive matter, and misanalyzes antitrust and tech platforms.
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate ...
The next generation software program

The papers in this issue provide an overview of the research fostered by the NSF Next Generation Software (NGS) Program, and some representative projects funded under the program. The NGS Program was announced in October of 1998, and with several calls for proposals between 1998 and 2004 has supported research in two broad technical thrusts. One program component has supported research for developing Technology for Performance Engineered Systems (TPES) for the Design, Management and Runtime Support of Computing Systems and Applications. The second program component, Complex Application Development and runtime Support Systems (CADSS) has sought to create new systems' software technology, including enhanced compiler capabilities, and tools for the development, runtime support and dynamic composition of complex applications executing on complex computing platforms, such as Computational Grids, assemblies of embedded systems and sensor systems, as well as high-end platforms (Grids-in-a-Box) and special purpose processing systems. Work along the directions of the NGS Program presently continues under the successor program, the NSF Computer Systems Research Program.
Mining the Data Tables for Lorentz and CPT Violation

In this conference proceedings, some comments on the present status and recent growth of efforts to find Lorentz and CPT violation are given by extracting metrics from the annually updated "Data Tables for Lorentz and CPT Violation." They reveal that tests span all the sectors of particle and gravitational physics, and have shown remarkable and consistent growth. Through numerous innovations and refinements in experiments, a large body of data has been amassed with ever-increasing precisions. The Tables are available through the Cornell preprint archive at arXiv:0801.0287.
Curve Shortening Flow and Smooth Projective Planes

In this paper, we study a family of curves on $S^2$ that defines a two-dimensional smooth projective plane. We use curve shortening flow to prove that any two-dimensional smooth projective plane can be smoothly deformed through a family of smooth projective planes into one which is isomorphic to the real projective plane. In addition, as a consequence of our main result, we show that any two smooth embedded curves on $RP^2$ which intersect transversally at exactly one point converge to two different geodesics under the flow.
Interleukin 17 and Senescent Cells Regulate the Foreign Body Response to Synthetic Material Implants in Mice and Humans

Medical devices and implants made of synthetic materials can induce an immune-mediated process when implanted in the body called the foreign body response, which results in formation of a fibrous capsule around the implant. To explore the immune and stromal connections underpinning the foreign body response, we analyzed fibrotic capsules surrounding surgically excised human breast implants from 12 individuals. We found increased numbers of interleukin 17 (IL17)-producing γδ+ T cells and CD4+ T helper 17 (TH17) cells as well as senescent stromal cells in the fibrotic capsules. Further analysis in a murine model demonstrated an early innate IL17 response to implanted synthetic material (polycaprolactone) particles that was mediated by innate lymphoid cells and γδ+ T cells. This was followed by a chronic adaptive CD4+ TH17 cell response that was antigen dependent. Synthetic materials with varying chemical and physical properties implanted either in injured muscle or subcutaneously induced similar IL17 responses in mice. Mice deficient in IL17 signaling established that IL17 was required for the fibrotic response to implanted synthetic materials and the development of p16INK4a senescent cells. IL6 produced by senescent cells was sufficient for the induction of IL17 expression in T cells. Treatment with a senolytic agent (navitoclax) that killed senescent cells reduced IL17 expression and fibrosis in the mouse implant model. Discovery of a feed-forward loop between the TH17 immune response and the senescence response to implanted synthetic materials introduces new targets for therapeutic intervention in the foreign body response.
HODET: Hybrid Object DEtection and Tracking using mmWave Radar and Visual Sensors

Image sensors have been explored heavily in automotive applications for collision avoidance and varying levels of autonomy. It requires a degree of brightness, therefore, the use of an image sensor in nighttime operation or dark conditions can be problematic along with challenging weather such as fog. Radar sensors have been employed to help cover the various environmental challenges with visible spectrum cameras. Edge computing technology has the potential to address a number of issues such as real-time processing requirements, off-loading of processing from congested servers, and size, weight, power, and cost (SWaP-C) constraints. This paper proposes a novel Hybrid Object DEtection and Tracking (HODET) using mmWave Radar and Visual Sensors at the edge. The HODET is a computing application of low SWaP-C electronics performing object detection, tracking and identification algorithms with the simultaneous use of image and radar sensors. While the machine vision camera alone could estimate the distance of an object, the radar sensor will provide an accurate distance and vector of movement. This additional data accuracy can be leveraged to further discriminate a detected object to protect against spoofing attacks. A real-world smart community public safety monitoring scenario is selected to verify the effectiveness of HODET, which detects, tracks objects of interests and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.
Ocular Hypertension Following Intravitreal Injection of 0.7mg Dexamethasone Implant versus 2mg Triamcinolone.

Background: To compare the incidence and outcomes of ocular hypertension (OHT) after intravitreal injection of 0.7 mg dexamethasone (DEX) and 2 mg triamcinolone acetonide (IVT).Methods: In a single...
Persistence by Habitat Dominance in the Annual Impatiens Capensis (Balsaminaceae)

Comparaison du comportement germinatif et du developpement et de la croissance des plantules d'I. capensis en presence de 2 especes perennes Eupatorium maculatum et Urtica dioica. On evalue aussi les possibilites d'etablissement de U. dioica dans les stations a I. capensis
Robust Estimation of the Mean with Bounded Relative Standard Deviation

Many randomized approximation algorithms operate by giving a procedure for simulating a random variable X which has mean \(\mu \) equal to the target answer, and a relative standard deviation bounded above by a known constant c. Examples of this type of algorithm includes methods for approximating the number of satisfying assignments to 2-SAT or DNF, the volume of a convex body, and the partition function of a Gibbs distribution. Because the answer is usually exponentially large in the problem input size, it is typical to require an estimate \(\hat{\mu }\) satisfy \({\mathbb {P}}(|\hat{\mu }/\mu - 1| > \varepsilon ) \le \delta \), where \(\varepsilon \) and \(\delta \) are user specified nonnegative parameters. The current best algorithm uses \(\left\lceil 2c^2\varepsilon ^{-2}(1+\varepsilon )^2 \ln (2/\delta ) \right\rceil \) samples to achieve such an estimate. By modifying the algorithm in order to balance the tails, it is possible to improve this result to \(\left\lceil 2(c^2\varepsilon ^{-2} + 1)/(1-\varepsilon ^2)\ln (2/\delta ) \right\rceil \) samples. Aside from the theoretical improvement, we also consider how to best implement this algorithm in practice. Numerical experiments show the behavior of the estimator on distributions where the relative standard deviation is unknown or infinite.
Sense of Smell

Olfactory receptor genes devoted to the sense of smell compose the largest multigene family in mammalian genomes. Although the number of receptor genes and related olfactory-specific genes vary in different species, up to 1% of the mammalian genome may be devoted exclusively to the sense of smell. This number reflects the importance of olfaction throughout vertebrate evolution, and the role of olfaction in maintaining a wide range of life-supporting activities, including feeding, nutrition, enhancement of gustatory responses, safety, predator/prey detection, and esthetics. In humans, ∼400 different genes encode functional olfactory receptor proteins that localize to the ciliary membranes of ∼6 000 000 receptor cells in the olfactory neuroepithelium only the visual system of vertebrates prossesses with more receptor cells. In this chapter, we review basic aspects of olfactory system anatomy, expression of odorant receptors within sensory neurons, and pathways of mammalian and invertebrate olfactory transduction. We focus primarily on the main (i.e. nonvomeronasal organ) olfactory system of vertebrates. In particular, specific attention is paid to the (1) chromosomal organization of olfactory receptor genes; (2) monogenic expression of olfactory receptor genes; (3) regulation of odorant receptor gene transcription within sensory neurons; (4) role of olfactory receptor gene expression in regulating the projection of functionally distinct olfactory sensory neurons from the olfactory epithelium to the olfactory bulb; (5) initial biochemical transduction events that include the molecular mechanisms responsible for stimulus transduction and generation of second messengers for signal amplification; and (6) microanatomy and function of olfactory bulb circuits. The plasticity of the olfactory system, as reflected by neurogenesis both within the olfactory neuroepithelium and the olfactory bulb is discussed in detail, as are a number of inherited and idiopathic human disorders associated with olfactory dysfunction in humans. Keywords: Anosmia; Gene Repertoire; Odor Coding; Olfaction; Receptor; Second Messenger; Smell
Lyapunov-Based Stabilization of MEMS Relays

In this paper, nonlinear stabilizers are introduced for voltage-controlled microelectromechanical system (MEMS) relays. The control constructions follow a Lyapunov approach and are based on a nonlinear dynamic model applicable to the two types of MEMS relays-electrostatic and electromagnetic. Two control schemes are presented with the objectives of avoiding pull-in during the microrelay closing and improving the transient response during the microrelay opening. First, an adaptive state feedback stabilizer is proposed to compensate for parametric uncertainty in all mechanical parameters and selected electrical parameters while ensuring asymptotic regulation of the electrode opening and closing. Next, a model-based observer/stabilizer is proposed to account for the lack of velocity measurements. Simulations demonstrate the performance of the two control schemes in comparison to the typical open-loop operation of the MEMS relay.
The distribution of free electrons in the inner Galaxy from pulsar dispersion measures

The dispersion measures of a sample of 149 pulsars in the inner Galaxy (absolute value of l 50 deg) were statistically analyzed to deduce the large-scale distribution of free thermal electrons in this region. The dispersion measure distribution of these pulsars shows significant evidence for a decrease in the electron scale height from a local value greater than the pulsar scale height to a value less than the pulsar scale height at galactocentric radii inside of approximately 7 kpc. An increase in the electron density (to a value around .15/cu cm at 4 to 5 kpc) must accompany such a decrease in scale height. There is also evidence for a large-scale warp in the electron distribution below the b + 0 deg plane inside the Solar circle. A model is proposed for the electron distribution which incorporates these features and Monte Carlo generated dispersion measure distributions are presented for parameters which best reproduce the observed pulsar distributions.
Global analysis of a predator–prey model with variable predator search rate

We consider a modified Holling-type II predator-prey model, based on the premise that the search rate of predators is dependent on the prey density, rather than constant. A complete analysis of the global behavior of the model is presented, and shows that the model exhibits a dichotomy similar to the classical Holling-type II model: either the coexistence steady state is globally stable; or it is unstable, and then a unique, globally stable limit cycle exists. We discuss the similarities, but also important differences between our model and the Holling-type II model. The main differences are that: 1. The paradox of enrichment which always occurs in the Holling-type II model, does not always occur here, and 2. Even when the paradox of enrichment occurs, predators can adapt by lowering their search rate, and effectively stabilize the system.
Analysis of Exchange Interactions in Dimers of Mn3 Single-Molecule Magnets, and their Sensitivity to External Pressure

In light of the potential use of single-molecule magnets (SMMs) in emerging quantum information science initiatives, we report first-principles calculations of the magnetic exchange interactions in...
Expression of a Real Matrix as a Difference of a Matrix and its Transpose Inverse

In this paper we derive a representation of an arbitrary real matrix M as the difference of a real matrix A and the transpose of its inverse. This expression may prove useful for progressing beyond known results for which the appearance of transpose-inverse terms prove to be obstacles, particularly in control theory and related applications such as computational simulation and analysis of matrix representations of articulated figures.
BLEURT: Learning Robust Metrics for Text Generation

Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgment. We propose BLEURT, a learned evaluation metric for English based on BERT. BLEURT can model human judgment with a few thousand possibly biased training examples. A key aspect of our approach is a novel pre-training scheme that uses millions of synthetic examples to help the model generalize. BLEURT provides state-of-the-art results on the last three years of the WMT Metrics shared task and the WebNLG data set. In contrast to a vanilla BERT-based approach, it yields superior results even when the training data is scarce and out-of-distribution.
Seqpare : a self-consistent metric of similarity between genomic interval sets

Searching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing sequences based on their interval sets. With this metric, the similarity of two interval sets is quantified by a single index, the ratio of their effective overlap over the union: an index of zero indicates unrelated interval sets, and an index of one means that the interval sets are identical. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric.
Deep learning-based automated image segmentation for concrete petrographic analysis

Abstract The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope. The high expertise and specialized equipment discourage this test for routine concrete quality control. Though the task can be alleviated with the aid of color-based image segmentation, additional surface color treatment is required. Recently, deep learning algorithms using convolutional neural networks (CNN) have achieved unprecedented segmentation performance on image testing benchmarks. In this study, we investigated the feasibility of using CNN to conduct concrete segmentation without the use of color treatment. The CNN demonstrated a strong potential to process a wide range of concretes, including those not involved in model training. The experimental results showed that CNN outperforms the color-based segmentation by a considerable margin, and has comparable accuracy to human experts. Furthermore, the segmentation time is reduced to mere seconds.
Asymmetry between Activators and Deactivators in Functional Protein Networks.

Are “turn-on” and “turn-off” functions in protein-protein interaction networks exact opposites of each other? To answer this question, we implement a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. We study the roles of activators and deactivators, two core components of oscillatory protein interaction networks, and find a striking asymmetry in the roles of activating and deactivating proteins, where activating proteins tend to be synergistic and deactivating proteins tend to be competitive.
Evaluation of M2-like macrophage enrichment after diffuse traumatic brain injury through transient interleukin-4 expression from engineered mesenchymal stromal cells

Appropriately modulating inflammation after traumatic brain injury (TBI) may prevent disabilities for the millions of those inflicted annually. In TBI, cellular mediators of inflammation, including macrophages and microglia, possess a range of phenotypes relevant for an immunomodulatory therapeutic approach. It is thought that early phenotypic modulation of these cells will have a cascading healing effect. In fact, an anti-inflammatory, “M2-like” macrophage phenotype after TBI has been associated with neurogenesis, axonal regeneration, and improved white matter integrity (WMI). There already exist clinical trials seeking an M2-like bias through mesenchymal stem/stromal cells (MSCs). However, MSCs do not endogenously synthesize key signals that induce robust M2-like phenotypes such as interleukin-4 (IL-4). To enrich M2-like macrophages in a clinically relevant manner, we augmented MSCs with synthetic IL-4 mRNA to transiently express IL-4. These IL-4 expressing MSCs (IL-4 MSCs) were characterized for expression and functionality and then delivered in a modified mouse TBI model of closed head injury. Groups were assessed for functional deficits and MR imaging. Brain tissue was analyzed through flow cytometry, multi-plex ELISA, qPCR, histology, and RNA sequencing. We observed that IL-4 MSCs indeed induce a robust M2-like macrophage phenotype and promote anti-inflammatory gene expression after TBI. However, here we demonstrate that acute enrichment of M2-like macrophages did not translate to improved functional or histological outcomes, or improvements in WMI on MR imaging. To further understand whether dysfunctional pathways underlie the lack of therapeutic effect, we report transcriptomic analysis of injured and treated brains. Through this, we discovered that inflammation persists despite acute enrichment of M2-like macrophages in the brain. The results demonstrate that MSCs can be engineered to induce a stronger M2-like macrophage response in vivo. However, they also suggest that acute enrichment of only M2-like macrophages after diffuse TBI cannot orchestrate neurogenesis, axonal regeneration, or improve WMI. Here, we also discuss our modified TBI model and methods to assess severity, behavioral studies, and propose that IL-4 expressing MSCs may also have relevance in other cavitary diseases or in improving biomaterial integration into tissues.
FMRP Regulates the Nuclear Export of Adam9 and Psen1 mRNAs: Secondary Analysis of an N 6-Methyladenosine Dataset

Fragile X mental retardation protein (FMRP) binds to and regulates the translation of amyloid-β protein precursor (App) mRNA, but the detailed mechanism remains to be determined. Differential methylation of App mRNA could underlie FMRP binding, message localization and translation efficiency. We sought to determine the role of FMRP and N6-methyladeonsine (m6A) on nuclear export of App mRNA. We utilized the m6A dataset by Hsu and colleagues to identify m6A sites in App mRNA and to determine if the abundance of message in the cytoplasm relative to the nucleus is altered in Fmr1 knockout mouse brain cortex. Given that processing of APP to Aβ and soluble APP alpha (sAPPα) contributes to disease phenotypes, we also investigated whether Fmr1KO associates with nuclear export of the mRNAs for APP protein processing enzymes, including β-site amyloid cleaving enzyme (Bace1), A disintegrin and metalloproteinases (Adams), and presenilins (Psen). Fmr1KO did not alter the nuclear/cytoplasmic abundance of App mRNA. Of 36 validated FMRP targets, 35 messages contained m6A peaks but only Agap2 mRNA was selectively enriched in Fmr1KO nucleus. The abundance of the APP processing enzymes Adam9 and Psen1 mRNA, which code for a minor alpha-secretase and gamma-secretase, respectively, were selectively enriched in wild type cytoplasm.
Dynamics for Spherical Spin Glasses: Disorder Dependent Initial Conditions

We derive the thermodynamic limit of the empirical correlation and response functions in the Langevin dynamics for spherical mixed p-spin disordered mean-field models, starting uniformly within one of the spherical bands on which the Gibbs measure concentrates at low temperature for the pure p-spin models and mixed perturbations of them. We further relate the large time asymptotics of the resulting coupled non-linear integro-differential equations, to the geometric structure of the Gibbs measures (at low temperature), and derive their FDT solution (at high temperature).