cssi/SciSciGPT-SciSciNet huggingface dataset:
Fibroblast Growth Factor Receptors Function Redundantly During Zebrafish Embryonic Development

Fibroblast growth factor (Fgf) signaling regulates many processes during development. In most cases, one tissue layer secretes an Fgf ligand that binds and activates an Fgf receptor (Fgfr) expressed by a neighboring tissue. Although studies have identified the roles of specific Fgf ligands during development, less is known about the requirements for the receptors. We have generated null mutations in each of the five fgfr genes in zebrafish. Considering the diverse requirements for Fgf signaling throughout development, and that null mutations in the mouse Fgfr1 and Fgfr2 genes are embryonic lethal, it was surprising that all zebrafish homozygous mutants are viable and fertile, with no discernable embryonic defect. Instead, we find that multiple receptors are involved in coordinating most Fgf-dependent developmental processes. For example, mutations in the ligand fgf8a cause loss of the midbrain-hindbrain boundary, whereas, in the fgfr mutants, this phenotype is seen only in embryos that are triple mutant for fgfr1a;fgfr1b;fgfr2, but not in any single or double mutant combinations. We show that this apparent fgfr redundancy is also seen during the development of several other tissues, including posterior mesoderm, pectoral fins, viscerocranium, and neurocranium. These data are an essential step toward defining the specific Fgfrs that function with particular Fgf ligands to regulate important developmental processes in zebrafish.
On Asymmetric Unification for the Theory of XOR with a Homomorphism

Asymmetric unification, or unification with irreducibility constraints, is a newly developed paradigm that arose out of the automated analysis of cryptographic protocols. However, there are still relatively few asymmetric unification algorithms. In this paper we address this lack by exploring the application of automata-based unification methods. We examine the theory of xor with a homomorphism, ACUNh, from the point of view of asymmetric unification, and develop a new automata-based decision procedure. Then, we adapt a recently developed asymmetric combination procedure to produce a general asymmetric-ACUNh decision procedure. Finally, we present a new approach for obtaining a solution-generating asymmetric-ACUNh unification automaton. We also compare our approach to the most commonly used form of asymmetric unification available today, variant unification.
Neural networks grown and self-organized by noise

Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can ‘grow’ a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct retinotopic pooling layers. Our approach is inspired by the mechanisms employed by the early visual system to wire the retina to the lateral geniculate nucleus (LGN), days before animals open their eyes. The key ingredients for robust self-organization are an emergent spontaneous spatiotemporal activity wave in the first layer and a local learning rule in the second layer that ‘learns’ the underlying activity pattern in the first layer. The algorithm is adaptable to a wide-range of input-layer geometries, robust to malfunctioning units in the first layer, and so can be used to successfully grow and self-organize pooling architectures of different pool-sizes and shapes. The algorithm provides a primitive procedure for constructing layered neural networks through growth and self-organization. We also demonstrate that networks grown from a single unit perform as well as hand-crafted networks on MNIST. Broadly, our work shows that biologically inspired developmental algorithms can be applied to autonomously grow functional `brains' in-silico.
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems With Limited Feedback Using ESPAR Antennas

We consider an opportunistic cognitive radio (CR) system consisting of a primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx), where SUtx is equipped with an electrically steerable parasitic array radiator (ESPAR) antenna with beam steering capability for sensing and communication, and there is a limited feedback channel from SUrx to SUtx. Taking a holistic approach, we develop a framework for integrated sector-based spectrum sensing and sector-based data communication. Upon sensing the channel busy, SUtx determines the beam corresponding to PU’s orientation. Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected beam corresponding to the strongest channel between SUtx and SUrx. We formulate a constrained optimization problem, where SUtx- SUrx link ergodic capacity is maximized, subject to average transmit power and interference constraints, and the optimization variables are sensing duration, thresholds of channel quantizer at SUrx, and transmit power levels at SUtx. Since this problem is non-convex we develop a suboptimal computationally efficient iterative algorithm to find the solution. Our numerical results quantify the capacity improvement provided by the ESPAR antenna and demonstrate that our CR system yields lower outage and symbol error probabilities, compared with a CR system that its SUtx has an omni-directional antenna.
Functional restoration of elbow flexion in nonobstetric brachial plexus injuries: A meta-analysis of nerve transfers versus grafts.

Restoration of elbow flexion is the priority in traumatic brachial plexus injuries. Surgical approaches commonly include nerve transfers and nerve grafting. Our objective was to evaluate the safety and efficacy profile of nerve transfers versus grafting for traumatic nonobstetric brachial plexus injuries. METHODS This systematic literature review was performed according to the PRISMA guidelines. A random-effects model meta-analysis was conducted, and the I-square was used to assess heterogeneity. The Medical Research Scale (MRC) score was used to assess the efficacy of the procedures. RESULTS Nine studies comprising 490 patients overall were identified. In the pooled analysis, functional recovery of elbow flexion defined as MRC ≥ M3, was superior in the transfer (N = 272/350, 77.7%) compared to the graft group (N = 99/140, 70.7%); however statistical significance was not reached (OR: 1.95; 95%CI: 0.79-4.83; I2 : 58.8%). However, the odds for successful restoration of elbow flexion (MRC≥M3) were significantly higher when the ulnar (OR:12.20; 95%CI:3.05-48.80; I2 :0%) or pectoral nerves (OR: 9.69; 95% CI: 1.83-51.25; I2 : 0%) were used as healthy donors for the transfer compared to the graft procedures. Results between the two groups were similar when the intercostal, spinal accessory, thoracodorsal, contralateral C7 and phrenic nerves were used as donors for the transfer procedures. CONCLUSIONS The ulnar or pectoral nerve transfer to musculocutaneous is associated with statistically significant superior rates of elbow flexion recovery as compared to graft. No differences were identified in the pooled analysis or the subgroups of other donors used in nerve transfers. Future randomized studies or prospective cohorts are needed to validate our results.
List Coloring a Cartesian Product with a Complete Bipartite Factor

We study the list chromatic number of the Cartesian product of any graph G and a complete bipartite graph with partite sets of size a and b, denoted \(\chi _\ell (G \square K_{a,b})\). We have two motivations. A classic result on the gap between list chromatic number and the chromatic number tells us \(\chi _\ell (K_{a,b}) = 1 + a\) if and only if \(b \ge a^a\). Since \(\chi _\ell (K_{a,b}) \le 1 + a\) for any \(b \in {\mathbb {N}}\), this result tells us the values of b for which \(\chi _\ell (K_{a,b})\) is as large as possible and far from \(\chi (K_{a,b})=2\). In this paper we seek to understand when \(\chi _\ell (G \square K_{a,b})\) is far from \(\chi (G \square K_{a,b}) = \max \{\chi (G), 2 \}\). It is easy to show \(\chi _\ell (G \square K_{a,b}) \le \chi _\ell (G) + a\). Borowiecki et al. (Discrete Math 306:1955–1958, 2006) showed that this bound is attainable if b is sufficiently large; specifically, \(\chi _\ell (G \square K_{a,b}) = \chi _\ell (G) + a\) whenever \(b \ge (\chi _\ell (G) + a - 1)^{a|V(G)|}\). Given any graph G and \(a \in {\mathbb {N}}\), we wish to determine the smallest b such that \(\chi _\ell (G \square K_{a,b}) = \chi _\ell (G) + a\). In this paper we show that the list color function, a list analogue of the chromatic polynomial, provides the right concept and tool for making progress on this problem. Using the list color function, we prove a general improvement on Borowiecki et al.’s (2006) result, and we compute the smallest such b for some large families of chromatic-choosable graphs.
Skewed macrophage polarization in aging skeletal muscle

Skeletal muscle aging is a major cause of disability and frailty in the elderly. The progressive impairment of skeletal muscle function with aging was recently linked to a disequilibrium between damage and repair. Macrophages participate in muscle tissue repair, first as pro-inflammatory M1 subtype and then as anti-inflammatory M2 subtype. However, information on the presence of macrophages in skeletal muscle is still sporadic and the effect of aging on macrophage phenotype remains unknown. In this study, we sought to characterize the polarization status of macrophages in skeletal muscle of persons across a wide range of ages. We found that most macrophages in human skeletal muscle are M2, and that this number increased with advancing age. On the contrary, M1 macrophages declined with aging, making the total number of macrophages invariant with older age. Notably, M2 macrophages colocalized with increasing intermuscular adipose tissue (IMAT) in aging skeletal muscle. Similarly, aged BALB/c mice showed increased IMAT and M2 macrophages in skeletal muscle, accompanied by slightly increased collagen protein production. Collectively, we report that polarization of macrophages to the major M2 subtype is associated with IMAT and propose that increased M2 in aged skeletal muscle may impact upon muscle metabolism associated with aging.
Aligning Needs: Integrating Citizen Science Efforts into Schools Through Service Requirements

Citizen science is the participation in scientific research by members of the public, and it is an increasingly valuable tool for both scientists and educators. For researchers, citizen science is a means of more quickly investigating questions which would otherwise be time-consuming and costly to study. For educators, citizen science offers a means to engage students in actual research and improve learning outcomes. Since most citizen science projects are usually designed with research goals in mind, many lack the necessary educator materials for successful integration in a formal science education (FSE) setting. In an ideal world, researchers and educators would build the necessary materials together; however, many researchers lack the time, resources, and networks to create these materials early on in the life of a citizen science project. For resource-poor projects, we propose an intermediate entry point for recruiting from the educational setting: community service or service learning requirements (CSSLRs). Many schools require students to participate in community service or service learning activities in order to graduate. When implemented well, CSSLRs provide students with growth and development opportunities outside the classroom while contributing to the community and other worthwhile causes. However, CSSLRs take time, resources, and effort to implement well. Just as citizen science projects need to establish relationships to transition well into formal science education, schools need to cultivate relationships with community service organizations. Students and educators at schools with CSSLRs where implementation is still a work in progress may be left with a burdensome requirement and inadequate support. With the help of a volunteer fulfilling a CSSLR, we investigated the number of students impacted by CSSLRs set at different levels of government and explored the qualifications needed for citizen science projects to fulfill CSSLRs by examining the explicitly-stated justifications for having CSSLRs, surveying how CSSLRs are verified, and using these qualifications to demonstrate how an online citizen science project, Mark2Cure, could use this information to meet the needs of students fulfilling CSSLRs.
A Roadmap to Interstellar Flight

In the nearly 60 years of spaceflight we have accomplished wonderful feats of exploration that have shown the incredible spirit of the human drive to explore and understand our universe. Yet in those 60 years we have barely left our solar system with the Voyager 1 spacecraft launched in 1977 finally leaving the solar system after 37 years of flight at a speed of 17 km/s or less than 0.006% the speed of light. As remarkable as this is we will never reach even the nearest stars with our current propulsion technology in even 10 millennium. We have to radically rethink our strategy or give up our dreams of reaching the stars, or wait for technology that does not currently exist. While we all dream of human spaceflight to the stars in a way romanticized in books and movies, it is not within our power to do so, nor it is clear that this is the path we should choose. We posit a technological path forward, that while not simple, it is within our technological reach. We propose a roadmap to a program that will lead to sending relativistic probes to the nearest stars and will open up a vast array of possibilities of flight both within our solar system and far beyond. Spacecraft from gram level complete spacecraft on a wafer ("wafersats") that reach more than _ c and reach the nearest star in 20 years to spacecraft with masses more than 105 kg (100 tons) that can reach speeds of greater than 1000 km/s. These systems can be propelled to speeds currently unimaginable with existing propulsion technologies. To do so requires a fundamental change in our thinking of both propulsion and in many cases what a spacecraft is. In addition to larger spacecraft, some capable of transporting humans, we consider functional spacecraft on a wafer, including integrated optical communications, imaging systems, photon thrusters, power and sensors combined with directed energy propulsion. The costs can be amortized over a very large number of missions beyond relativistic spacecraft as such planetary defense, beamed energy for distant spacecraft, sending power back to Earth, stand-off composition analysis of solar system targets, long range laser communications, SETI searches and even terra forming. The human factor of exploring the nearest stars and exo-planets would be a profound voyage for humanity, one whose non-scientific implications would be enormous. It is time to begin this inevitable journey far beyond our home.
Fault-tolerant Covariance Intersection for localizing robot swarms

Abstract This paper examines the important problem of cooperative localization in robot swarms, in the presence of unmodeled errors experienced by real sensors in hardware platforms. Many existing methods for cooperative swarm localization rely on approximate distance metric heuristics based on properties of the communication graph. We present a new cooperative localization method that is based on a rigorous and scalable treatment of estimation errors generated by peer-to-peer sharing of relative robot pose information. Our approach blends Covariance Intersection and Covariance Union techniques from distributed sensor fusion theory in a novel way, in order to maintain statistical estimation consistency for cooperative localization errors. Experimental validation results show that this approach provides both reliable and accurate state estimation results for Droplet swarms in scenarios where other existing swarm localization methods cannot.
Parallel wireless mobile relays with space-time modulations

We use Hurwitz-Radon matrices to construct a space-time code for parallel wireless mobile relays that are located anywhere between a source and a destination. Each relay receives a sequence of symbols (i.e., baseband signals corrupted by fading and noise) simultaneously from the source. These symbols are not decoded into information bits at the relays but are rearranged (i.e., space-time modulated) in their orders, amplitudes and phases according to the Hurwitz-Radon code. The relays do not exchange symbols with each other but forward the modified sequences of symbols in parallel to the destination. Our study shows that with R relays, an order of diversity around R/2 can be achieved, i.e., the averaged bit error rate is in the order of 1/SNR/sup R/2/ as opposed to 1/SNR for a single (regenerative) relay system. More than 10 dB power saving, from the baseline of the single relay system, can be achieved with eight (non-regenerative) relays.
Time-resolved dynamics in iodide-uracil-water clusters upon excitation of the nucleobase.

The dynamics of iodide-uracil-water (I−·U·H2O) clusters following π-π* excitation of the nucleobase are probed using time-resolved photoelectron spectroscopy. Photoexcitation of this cluster at 4.77 eV results in electron transfer from the iodide moiety to the uracil, creating a valence-bound anion within the cross correlation of the pump and probe laser pulses. This species can decay by a number of channels, including autodetachment and dissociation to I− or larger anion fragments. Comparison of the energetics of the photoexcited cluster and its decay dynamics with those of the bare iodide-uracil (I−·U) complex provides a sensitive probe of the effects of microhydration on these species.The dynamics of iodide-uracil-water (I−·U·H2O) clusters following π-π* excitation of the nucleobase are probed using time-resolved photoelectron spectroscopy. Photoexcitation of this cluster at 4.77 eV results in electron transfer from the iodide moiety to the uracil, creating a valence-bound anion within the cross correlation of the pump and probe laser pulses. This species can decay by a number of channels, including autodetachment and dissociation to I− or larger anion fragments. Comparison of the energetics of the photoexcited cluster and its decay dynamics with those of the bare iodide-uracil (I−·U) complex provides a sensitive probe of the effects of microhydration on these species.
Weak neural signatures of spatial selective auditory attention in hearing-impaired listeners.

Spatial attention may be used to select target speech in one location while suppressing irrelevant speech in another. However, if perceptual resolution of spatial cues is weak, spatially focused attention may work poorly, leading to difficulty communicating in noisy settings. In electroencephalography (EEG), the distribution of alpha (8–14 Hz) power over parietal sensors reflects the spatial focus of attention [Banerjee, Snyder, Molholm, and Foxe (2011). J. Neurosci. 31, 9923–9932; Foxe and Snyder (2011). Front. Psychol. 2, 154.] If spatial attention is degraded, however, alpha may not be modulated across parietal sensors. A previously published behavioral and EEG study found that, compared to normal-hearing (NH) listeners, hearing-impaired (HI) listeners often had higher interaural time difference thresholds, worse performance when asked to report the content of an acoustic stream from a particular location, and weaker attentional modulation of neural responses evoked by sounds in a mixture [Dai, Best, and Shinn-Cunningham (2018). Proc. Natl. Acad. Sci. U. S. A. 115, E3286]. This study explored whether these same HI listeners also showed weaker alpha lateralization during the previously reported task. In NH listeners, hemispheric parietal alpha power was greater when the ipsilateral location was attended; this lateralization was stronger when competing melodies were separated by a larger spatial difference. In HI listeners, however, alpha was not lateralized across parietal sensors, consistent with a degraded ability to use spatial features to selectively attend.Spatial attention may be used to select target speech in one location while suppressing irrelevant speech in another. However, if perceptual resolution of spatial cues is weak, spatially focused attention may work poorly, leading to difficulty communicating in noisy settings. In electroencephalography (EEG), the distribution of alpha (8–14 Hz) power over parietal sensors reflects the spatial focus of attention [Banerjee, Snyder, Molholm, and Foxe (2011). J. Neurosci. 31, 9923–9932; Foxe and Snyder (2011). Front. Psychol. 2, 154.] If spatial attention is degraded, however, alpha may not be modulated across parietal sensors. A previously published behavioral and EEG study found that, compared to normal-hearing (NH) listeners, hearing-impaired (HI) listeners often had higher interaural time difference thresholds, worse performance when asked to report the content of an acoustic stream from a particular location, and weaker attentional modulation of neural responses evoked by sounds in a mixture [Dai, Best, a...
An approach to model validation in the /spl mu/ framework

The problem of model validation of postulated system models when experimental data is available is addressed in this paper. The approach presented requires a /spl mu/ test across frequency and shows that the structure of the model set in terms of the size and type of uncertainty and noise weights plays a central role in the model validation process. In general, increasing the size of the model set increases the ease of validation of a particular model. The proposed model-validation technique is used to synthesize controllers for an experimental structure which significantly out performs poorly selected uncertainty weights.
Preventing and Treating Heart Failure with Sodium-Glucose Co-Transporter 2 Inhibitors

Abstract Heart failure is a common complication among patients with type 2 diabetes mellitus and is associated with significantly increased risks of subsequent morbidity and mortality. Until recently, therapies and strategies were lacking to attenuate this excess risk of heart failure in this population. Sodium-glucose co-transporter 2 (SGLT2) inhibitors represent a unique class of glucose-lowering therapies that have multisystem health benefits. Three large cardiovascular outcomes trials have demonstrated consistent reductions in heart failure events among patients with type 2 diabetes mellitus with, or at risk for, atherosclerotic cardiovascular disease. Another trial recently showed that an SGLT2 inhibitor, canagliflozin, also significantly reduced heart failure events among patients with type 2 diabetes mellitus and albuminuric chronic kidney disease. The SGLT2 inhibitor class represents an important new therapeutic approach for the prevention of heart failure in at-risk patients with type 2 diabetes mellitus, and is actively being studied for use in treating patients with heart failure (with or without type 2 diabetes mellitus). (Supplementary video “Preventing and Treating Heart Failure with Sodium-Glucose Co-Transporter 2 Inhibitors” is available online .)
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.
Complexity of Strong Approximation on the Sphere

By assuming some widely-believed arithmetic conjectures, we show that the task of accepting a number that is representable as a sum of $d\geq2$ squares subjected to given congruence conditions is NP-complete. On the other hand, we develop and implement a deterministic polynomial-time algorithm that represents a number as a sum of 4 squares with some restricted congruence conditions, by assuming a polynomial-time algorithm for factoring integers and Conjecture~\ref{cc}. As an application, we develop and implement a deterministic polynomial-time algorithm for navigating LPS Ramanujan graphs, under the same assumptions.
Prediction of site specific coverage and cell shape for outdoor microcells

The design of future wireless communication systems employing microcells will require a flexible tool for determining the coverage area based on site specific features. In this paper, the accuracy of a ray based algorithm for predicting the sector average path loss in residential and high rise environments is investigated by comparison with measurements. Measurements were taken in residential and in high rise environments. In the residential environment building heights ranged from 1 to 3 stories and the base station antenna was near rooftop levels, propagation between the base station and the mobile consisted mainly of paths which went over the intervening buildings. This was validated by good agreement with the measurements for the antenna above the rooftops, and fair agreement for the antenna below the rooftops. In the high rise environment the buildings were significantly higher than the base station antenna, so that the dominant contributions to the signal at the receiver are due to propagation around the buildings. Once again the predictions corresponded well the measurements.
On the Experience and Engineering of Consumer Pride, Consumer Excitement, and Consumer Relaxation in the Marketplace

Abstract This article presents new conceptual and managerial insights about consumer experiences of positive emotions in the marketplace and how to engineer these emotional experiences for business purposes. Specifically, we provide an in-depth conceptual analysis of three positive emotions that are of high relevance for marketers: (1) consumer pride, (2) consumer excitement, and (3) consumer relaxation. Through a systematic deconstruction of numerous consumer accounts of emotional experiences, combined with a careful theoretical analysis, we (a) codify how these emotions typically arise in consumption contexts, (b) identify their primary drivers and moderators in marketplace settings, including retail environments, and (c) explain how these theoretical insights can be used to engineer pride, excitement, and relaxation in consumption journeys.
Saturation Mutagenesis Genome Engineering of Infective ΦX174 Bacteriophage via Unamplified Oligo Pools and Golden Gate Assembly

Here we present a novel protocol for the construction of saturation single-site—and massive multisite—mutant libraries of a bacteriophage. We segmented the ΦX174 genome into 14 nontoxic and nonrepl...
Dry sediment loading of headwater channels fuels post-wildfire debris flows in bedrock landscapes

Landscapes following wildfire commonly have significant increases in sediment yield and debris flows that pose major hazards and are difficult to predict. Ultimately, post-wildfire sediment yield is governed by processes that deliver sediment from hillslopes to channels, but it is commonly unclear the degree to which hillslope sediment delivery is driven by wet versus dry processes, which limits the ability to predict debris-flow occurrence and response to climate change. Here we use repeat airborne lidar topography to track sediment movement following the 2009 CE Station Fire in southern California, USA, and show that post-wildfire debris flows initiated in channels filled by dry sediment transport, rather than on hillsides during rainfall as typically assumed. We found widespread patterns of 1–3 m of dry sediment loading in headwater channels immediately following wildfire and before rainfall, followed by sediment excavation during subsequent storms. In catchments where post-wildfire dry sediment loading was absent, possibly due to differences in lithology, channel scour during storms did not occur. Our results support a fire-flood model in bedrock landscapes whereby debris-flow occurrence depends on dry sediment loading rather than hillslope-runoff erosion, shallow landslides, or burn severity, indicating that sediment supply can limit debris-flow occurrence in bedrock landscapes with more-frequent fires.
Revisiting Challenges in Data-to-Text Generation with Fact Grounding

Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source. To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box- and line-score tables. However, limited attempts have been made in this direction and the challenges remain. We observe a prominent bottleneck in the corpus where only about 60% of the summary contents can be grounded to the boxscore records. Such information deficiency tends to misguide a conditioned language model to produce unconditioned random facts and thus leads to factual hallucinations. In this work, we restore the information balance and revamp this task to focus on fact-grounded data-to-text generation. We introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding), with 50% more data from the year 2017-19 and enriched input tables, and hope to attract research focuses in this direction. Moreover, we achieve improved data fidelity over the state-of-the-art models by integrating a new form of table reconstruction as an auxiliary task to boost the generation quality.
The emergence of isolated coherent vortices in turbulent flow

A study is made of some numerical calculations of two-dimensional and geostrophic turbulent flows. The primary result is that, under a broad range of circumstances, the flow structure has its vorticity concentrated in a small fraction of the spatial domain, and these concentrations typically have lifetimes long compared with the characteristic time for nonlinear interactions in turbulent flow (i.e. an eddy turnaround time). When such vorticity concentrations occur, they tend to assume an axisymmetric shape and persist under passive advection by the large-scale flow, except for relatively rare encounters with other centres of concentration. These structures can arise from random initial conditions without vorticity concentration, evolving in the midst of what has been traditionally characterized as the ‘cascade’ of isotropic, homogeneous, large-Reynolds-number turbulence: the systematic elongation of isolines of vorticity associated with the transfer of vorticity to smaller scales, eventually to dissipation scales, and the transfer of energy to larger scales. When the vorticity concentrations are a sufficiently dominant component of the total vorticity field, the cascade processes are suppressed. The demonstration of persistent vorticity concentrations on intermediate scales - smaller than the scale of the peak of the energy spectrum and larger than the dissipation scales - does not invalidate many of the traditional characterizations of two-dimensional and geostrophic turbulence, but I believe it shows them to be substantially incomplete with respect to a fundamental phenomenon in such flows.
Eigenvalue Distribution in the Self-Dual Non-Hermitian Ensemble

We consider an ensemble of self-dual matrices with arbitrary complex entries. This ensemble is closely related to a previously defined ensemble of anti-symmetric matrices with arbitrary complex entries. We study the two-level correlation functions numerically. Although no evidence of non-monotonicity is found in the real space correlation function, a definite shoulder is found. On the analytical side, we discuss the relationship between this ensemble and the β=4 two-dimensional one-component plasma, and also argue that this ensemble, combined with other ensembles, exhausts the possible universality classes in non-hermitian random matrix theory. This argument is based on combining the method of hermitization of Feinberg and Zee with Zirnbauer's classification of ensembles in terms of symmetric spaces.
A tale of santa claus, hypergraphs and matroids

A well-known problem in scheduling and approximation algorithms is the Santa Claus problem. Suppose that Santa Claus has a set of gifts, and he wants to distribute them among a set of children so that the least happy child is made as happy as possible. Here, the value that a child i has for a present j is of the form pij ∈ 0, pj In this paper, we introduce a matroid version of the Santa Claus problem. Our algorithm is also based on Haxell's augmenting tree, but with the introduction of the matroid structure we solve a more general problem with cleaner methods. Our result can then be used as a blackbox to obtain a (4 + e)-approximation for Santa Claus. This factor also compares against a natural, compact LP for Santa Claus.
Conditional Generative Neural System for Probabilistic Trajectory Prediction

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control. Due to the uncertain nature of the future, it is desired to make inference from a probability perspective instead of deterministic prediction. In this paper, we propose a conditional generative neural system (CGNS) for probabilistic trajectory prediction to approximate the data distribution, with which realistic, feasible and diverse future trajectory hypotheses can be sampled. The system combines the strengths of conditional latent space learning and variational divergence minimization, and leverages both static context and interaction information with soft attention mechanisms. We also propose a regularization method for incorporating soft constraints into deep neural networks with differentiable barrier functions, which can regulate and push the generated samples into the feasible regions. The proposed system is evaluated on several public benchmark datasets for pedestrian trajectory prediction and a roundabout naturalistic driving dataset collected by ourselves. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction accuracy.
Absolute pressure and gas species identification with an optically levitated rotor

The authors describe a novel variety of spinning-rotor vacuum gauge in which the rotor is a ∼ 4.7 − μ m − diameter silica microsphere, optically levitated. A rotating electrostatic field is used to apply torque to the permanent electric dipole moment of the silica microsphere and control its rotational degrees of freedom. When released from a driving field, the microsphere’s angular velocity decays exponentially with a damping time inversely proportional to the residual gas pressure and dependent on gas composition. The gauge is calibrated by measuring the rotor mass with electrostatic co-levitation and assuming a spherical shape, confirmed separately, and uniform density. The gauge is cross-checked against a capacitance manometer by observing the torsional drag due to a number of different gas species. The techniques presented can be used to perform absolute vacuum measurements localized in space, owing to the small dimensions of the microsphere and the ability to translate the optical trap in three dimensions, as well as measurements in magnetic field environments. In addition, the dynamics of the microsphere, paired with a calibrated vacuum gauge, can be used to measure the effective molecular mass of a gas mixture without the need for ionization and at pressures up to approximately 1 mbar.The authors describe a novel variety of spinning-rotor vacuum gauge in which the rotor is a ∼ 4.7 − μ m − diameter silica microsphere, optically levitated. A rotating electrostatic field is used to apply torque to the permanent electric dipole moment of the silica microsphere and control its rotational degrees of freedom. When released from a driving field, the microsphere’s angular velocity decays exponentially with a damping time inversely proportional to the residual gas pressure and dependent on gas composition. The gauge is calibrated by measuring the rotor mass with electrostatic co-levitation and assuming a spherical shape, confirmed separately, and uniform density. The gauge is cross-checked against a capacitance manometer by observing the torsional drag due to a number of different gas species. The techniques presented can be used to perform absolute vacuum measurements localized in space, owing to the small dimensions of the microsphere and the ability to translate the optical trap in three ...
Overpowered and Underprotected Toys Empowering Parents with Tools to Protect Their Children

The Internet of Things (IoT) has penetrated the global market including that of children’s toys. Worldwide, Smart Toy sales have reached $9 billion in 2019 and is expected to exceed $15 billion by 2022. Connecting IoT toys to the internet exposes users and their data to multivariate risk due to device vulnerabilities. When IoT devices are marketed to individuals, especially children, the potential for negative impact is significant, so their design must result in robust security implementations. For our study, we performed penetration testing on a Fisher- Price Smart Toy. We were able to obtain root access to the device, capture live pictures and videos, as well as install remote access software which allows surreptitious recordings over WiFi network connections without user knowledge or permission. We propose solutions including adhering to rudimentary standards for security design in toys, a mobile application for IoT threat assessment and user education, and an ambient risk communication tool aligned with user risk perception. The proposed solutions are crucial to empower users with capabilities to identify and understand ambient risks and defend against malicious activities.
Uplink Non-Orthogonal Multiple Access Over Mixed RF-FSO Systems

In this paper, we consider a relay-assisted uplink non-orthogonal multiple access (NOMA) system. In this system, two radio frequency (RF) users are grouped for simultaneous transmissions, over each resource block, to an intermediate relay. The relay then forwards the amplified version of the users’ aggregated signals, in the presence of multiuser interference, to a relatively far destination. In order to cope with the users’ ever-increasing desire for higher data rates, a high-throughput free-space optics (FSO) link is employed as the relay-destination backhaul link. It is assumed that the FSO backhaul link is subject to Gamma-Gamma turbulence with pointing error. Also, a Rayleigh fading model is considered for the user-relay access links. Under these assumptions, we derive closed-form expressions for the outage probability and tractable forms, involving only one-dimensional integrals, for the ergodic capacity. Moreover, the outage probability and ergodic capacity analysis are extended to the conventional RF-backhauled systems in the presence of multiuser interference to both relay and destination nodes, and Rician fading for the relay-destination RF link. Our results reveal the superiority of FSO backhauling for high-throughput and high-reliability NOMA systems compared to RF backhauling. This work can be considered as a general analysis of dual-hop uplink NOMA systems as well as the first attempt to incorporate power-domain NOMA in mixed RF-FSO systems.
Testing of the LSST's photometric calibration strategy at the CTIO 0.9 meter telescope

The calibration hardware system of the Large Synoptic Survey Telescope (LSST) is designed to measure two quantities: a telescope’s instrumental response and atmospheric transmission, both as a function of wavelength. First of all, a “collimated beam projector” is designed to measure the instrumental response function by projecting monochromatic light through a mask and a collimating optic onto the telescope. During the measurement, the light level is monitored with a NIST-traceable photodiode. This method does not suffer from stray light effects or the reflections (known as ghosting) present when using a flat-field screen illumination, which has a systematic source of uncertainty from uncontrolled reflections. It allows for an independent measurement of the throughput of the telescope’s optical train as well as each filter’s transmission as a function of position on the primary mirror. Second, CALSPEC stars can be used as calibrated light sources to illuminate the atmosphere and measure its transmission. To measure the atmosphere’s transfer function, we use the telescope’s imager with a Ronchi grating in place of a filter to configure it as a low resolution slitless spectrograph. In this paper, we describe this calibration strategy, focusing on results from a prototype system at the Cerro Tololo Inter-American Observatory (CTIO) 0.9 meter telescope. We compare the instrumental throughput measurements to nominal values measured using a laboratory spectrophotometer, and we describe measurements of the atmosphere made via CALSPEC standard stars during the same run.
On Ball Dentable Property in Banach Spaces

In this work, we introduce the notion of Ball dentable property in Banach spaces. We study certain stability results for the \(w^*\)-Ball dentable property leading to a discussion on Ball rentability in the context of ideals of Banach spaces. We prove that the \(w^*\)-Ball-dentable property can be lifted from an M-ideal to the whole Banach Space. We also prove similar results for strict ideals of a Banach space. We note that the space \(C(K,X)^*\) has \(w^*\)-Ball dentable property when K is dispersed and \(X^*\) has the \(w^*\)-Ball dentable property.
A New On-Line Robust Approach to Design Noise-Immune Speech Recognition Systems

Hereafter, we present a new approach dealing to cope with the harmful effects of noise on speech recognition systems (SRS). This approach is oriented to hardware redundancy and it is essentially a modification of the classic Recovery Blocks scheme. When compared to conventional approaches using Fast Fourier Transform (FFT) and Hamming Code, the primary benefit of such a technique is to improve system performance when operating in real (i.e., noisy) environments. The second advantage is related to the considerably low complexity and reduced area overhead required for implementation. We implemented three full versions of the proposed algorithm: one running of a PC microcomputer, and a second one slightly modified to run on a TMS-320C67 Texas DSP microprocessor module. Both of them were described in the C language. Finally, a last implementation was prototyped on a HW-SW development environment based on the same Texas microprocessor and on the FLEX10K20 FPGA Altera Component.
A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization

We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function-the agents' sum-utility-plus a nonsmooth (extended-valued) convex one. We propose a general algorithmic framework for such a class of problems and provide a unified convergence analysis leveraging the theory of operator splitting. Our results unify several approaches proposed in the literature of distributed optimization for special instances of our formulation. Distinguishing features of our scheme are: (i) when the agents' functions are strongly convex, the algorithm converges at a linear rate, whose dependencies on the agents' functions and the network topology are decoupled, matching the typical rates of centralized optimization; (ii) the step-size does not depend on the network parameters but only on the optimization ones; and (iii) the algorithm can adjust the ratio between the number of communications and computations to achieve the same rate of the centralized proximal gradient scheme (in terms of computations). This is the first time that a distributed algorithm applicable to composite optimization enjoys such properties.
Blockchain-Powered Software Defined Network-Enabled Networking Infrastructure for Cloud Management

Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios.
Input perturbations for adaptive control and learning

Abstract This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems. It proposes practical, easy to implement control policies based on perturbations of input signals. Such policies are shown to achieve a worst-case regret that scales as the square-root of the time horizon, and holds uniformly over time. Further, it discusses specific settings where such greedy policies attain the information theoretic lower bound of logarithmic regret. To establish the results, recent advances on self-normalized martingales together with a novel method of policy decomposition are leveraged.
The Graphon Fourier Transform

In many network problems, graphs may change by the addition of nodes, or the same problem may need to be solved in multiple similar graphs. This generates inefficiency, as analyses and systems that are not transferable have to be redesigned. To address this, we consider graphons, which are both limit objects of convergent graph sequences and random graph models. We define graphon signals and introduce the Graphon Fourier Transform (WFT), to which the Graph Fourier Transform (GFT) is shown to converge. This result is demonstrated in two numerical experiments where, as expected, the GFT converges, hinting to the possibility of centralizing analysis and design on graphons to leverage transferability.
Sharp weighted Sobolev trace inequalities and fractional powers of the Laplacian

Abstract We establish a family of sharp Sobolev trace inequalities involving the W k , 2 ( R + n + 1 , y a ) -norm. These inequalities are closely related to the realization of fractional powers of the Laplacian on R n = ∂ R + n + 1 as generalized Dirichlet-to-Neumann operators associated to powers of the weighted Laplacian in upper half space, generalizing observations of Caffarelli–Silvestre and of Yang.
The Influence of Audit Committee Expertise on Firms' Internal Controls: Evidence from Mergers and Acquisitions

This study examines how audit committee expertise influences firms' key internal control scoping decisions. Using a unique M&A setting where the internal control audit is voluntary, I study whether...