cssi/SciSciGPT-SciSciNet huggingface dataset:
Hypomodified tRNA in evolutionarily distant yeasts can trigger rapid tRNA decay to activate the general amino acid control response, but with different consequences

All tRNAs are extensively modified, and modification deficiency often results in growth defects in the budding yeast Saccharomyces cerevisiae and neurological or other disorders in humans. In S. cerevisiae, lack of any of several tRNA body modifications results in rapid tRNA decay (RTD) of certain mature tRNAs by the 5'-3' exonucleases Rat1 and Xrn1. As tRNA quality control decay mechanisms are not extensively studied in other eukaryotes, we studied trm8Δ mutants in the evolutionarily distant fission yeast Schizosaccharomyces pombe, which lack 7-methylguanosine at G46 (m7G46) of their tRNAs. We report here that S. pombe trm8Δ mutants are temperature sensitive primarily due to decay of tRNATyr(GUA) and that spontaneous mutations in the RAT1 ortholog dhp1+ restored temperature resistance and prevented tRNA decay, demonstrating conservation of the RTD pathway. We also report for the first time evidence linking the RTD and the general amino acid control (GAAC) pathways, which we show in both S. pombe and S. cerevisiae. In S. pombe trm8Δ mutants, spontaneous GAAC mutations restored temperature resistance and tRNA levels, and the trm8Δ temperature sensitivity was precisely linked to GAAC activation due to tRNATyr(GUA) decay. Similarly, in the well-studied S. cerevisiae trm8Δ trm4Δ RTD mutant, temperature sensitivity was closely linked to GAAC activation due to tRNAVal(AAC) decay; however, in S. cerevisiae, GAAC mutations increased tRNA loss and exacerbated temperature sensitivity. A similar exacerbated growth defect occurred upon GAAC mutation in S. cerevisiae trm8Δ and other single modification mutants that triggered RTD. Thus, these results demonstrate a conserved GAAC activation coincident with RTD in S. pombe and S. cerevisiae, but an opposite impact of the GAAC response in the two organisms. We speculate that the RTD pathway and its regulation of the GAAC pathway is widely conserved in eukaryotes, extending to other mutants affecting tRNA body modifications.
Post-anticoagulant D-dimer is a highly prognostic biomarker of COVID-19 mortality

Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe coronavirus disease 2019 (COVID-19) illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality. Adult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City (NY, USA). We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests. 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend group; 27% for the high-decrease group; 21% for the low-increase group; and 9% for the low-decrease group (p<0.001). Using penalised logistic regression models to simultaneously analyse 67 clinical variables, the high increase (adjusted odds ratios (ORadj): 6.58, 95% CI 3.81-11.16), low increase (ORadj: 4.06, 95% CI 2.23-7.38) and high decrease (ORadj: 2.37; 95% CI 1.37-4.09) D-dimer groups (reference: low decrease group) had the highest odds for in-hospital mortality among all clinical features. Changes in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.
Trans-Planckian Censorship and the Swampland

In this paper, we propose a new Swampland condition, the Trans-Planckian Censorship Conjecture (TCC), based on the idea that in a consistent quantum theory of gravity sub-Planckian quantum fluctuations should remain quantum and never become larger than the Hubble horizon and freeze in an expanding universe. Applied to the case of scalar fields, it leads to conditions that are similar to the refined dS Swampland conjecture. For large field ranges, TCC is stronger than the dS Swampland conjecture but it is weaker for small field ranges. In particular for asymptotic regions of field space, TCC leads to a bound $$ \left|V^{\prime}\right|\ge \frac{2}{\sqrt{\left(d-1\right)\left(d-2\right)}}V, $$ which is consistent with all known cases in string theory. Like the dS Swampland conjecture, the TCC forbids long-lived meta-stable dS spaces, but it does allow sufficiently short-lived ones.
Horizontal 'gene drives' harness indigenous bacteria for bioremediation.

Engineering bacteria to clean-up oil spills is rapidly advancing but faces regulatory hurdles and environmental concerns. Here, we develop a new technology to harness indigenous soil microbial communities for bioremediation by flooding local populations with catabolic genes for petroleum hydrocarbon degradation. Overexpressing three enzymes (almA, xylE, p450cam) in Escherichia coli led to degradation of 60–99% of target hydrocarbon substrates. Mating experiments, fluorescence microscopy and TEM revealed indigenous bacteria could obtain these vectors from E. coli through several mechanisms of horizontal gene transfer (HGT), including conjugation and cytoplasmic exchange through nanotubes. Inoculating petroleum-polluted sediments with E. coli carrying the vector pSF-OXB15-p450camfusion showed that the E. coli cells died after five days but a variety of bacteria received and carried the vector for over 60 days after inoculation. Within 60 days, the total petroleum hydrocarbon content of the polluted soil was reduced by 46%. Pilot experiments show that vectors only persist in indigenous populations when under selection pressure, disappearing when this carbon source is removed. This approach to remediation could prime indigenous bacteria for degrading pollutants while providing minimal ecosystem disturbance.
Urban sensing as a random search process

Abstract We study a new random search process: the taxi drive. The motivation for this process comes from urban sensing in which sensors are mounted on moving vehicles such as taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi drive is composed of both random and regular parts: passengers are brought to randomly chosen locations via deterministic (i.e. shortest paths) routes. We show through a numerical study that this hybrid motion endows the taxi drive with advantageous spreading properties. In particular, on certain graph topologies it offers reduced cover times compared to random walks and persistent random walks.
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS fields suffer from imbalanced and missing sample data, which makes the training of IDS difficult. In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones. G-IDS also fixes the difficulties of imbalanced or missing data problems. We model a network security dataset for an emerging CPS using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. We find that our proposed G-IDS model performs much better in attack detection and model stabilization during the training process than a standalone IDS.
Accelerated Robot Learning via Human Brain Signals

In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning signal is mostly zeros. In contrast, humans are good at assessing and predicting the future consequences of actions and can serve as good reward/policy shapers to accelerate the robot learning process. Previous works have shown that the human brain generates an error-related signal, measurable using electroencephelography (EEG), when the human perceives the task being done erroneously. In this work, we propose a method that uses evaluative feedback obtained from human brain signals measured via scalp EEG to accelerate RL for robotic agents in sparse reward settings. As the robot learns the task, the EEG of a human observer watching the robot attempts is recorded and decoded into noisy error feedback signal. From this feedback, we use supervised learning to obtain a policy that subsequently augments the behavior policy and guides exploration in the early stages of RL. This bootstraps the RL learning process to enable learning from sparse reward. Using a simple robotic navigation task as a test bed, we show that our method achieves a stable obstacle-avoidance policy with high success rate, outperforming learning from sparse rewards only that struggles to achieve obstacle avoidance behavior or fails to advance to the goal.
BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of the brain and the spinal cord, which leads to paralysis of motor functions. Patients retain their ability to blink, which can be used for communication. Here, We present an Artificial Intelligence (AI) system that uses eye-blinks to communicate with the outside world, running on real-time Internet-of-Things (IoT) devices. The system uses a Convolutional Neural Network (CNN) to find the blinking pattern, which is defined as a series of Open and Closed states. Each pattern is mapped to a collection of words that manifest the patient’s intent. To investigate the best trade-off between accuracy and latency, we investigated several Convolutional Network architectures, such as ResNet, SqueezeNet, DenseNet, and InceptionV3, and evaluated their performance. We found that the InceptionV3 architecture, after hyper-parameter fine-tuning on the specific task led to the best performance with an accuracy of 99.20% and 94 ms latency. This work demonstrates how the latest advances in deep learning architectures can be adapted for clinical systems that ameliorate the patient’s quality of life regardless of the point-of-care.
Microbial community assembly in a multi-layer dendritic metacommunity

A major goal of metacommunity ecology is to infer the local- and regional-scale processes that underlie community assembly. In dendritic ecological networks, branching patterns and directional flow can alter the balance between local and regional factors during assembly. Vertical habitat structure may further affect community assembly in dendritic metacommunities. In this study, we analyzed the bacterial metacommunity of a fifth-order mountain stream network to assess differences in community assembly (1) between planktonic and benthic habitats, (2) across spatial scales, and (3) between headwater and downstream regions of the network. Using taxonomic and phylogenetic null modeling, we found habitat-specific spatial patterns of community assembly across the dendritic network. Compositional differences between planktonic and benthic communities were maintained by variable selection, but we also found evidence of local dispersal limitation between the two habitats. Planktonic community assembly was scale dependent, transitioning from homogeneous selection at local scales to variable selection at regional scales, while benthic community assembly was less scale dependent. Variable selection structured headwaters in both habitat types, but downstream communities were primarily structured by homogeneous selection, especially in sediments. Taken together, our results show that vertical habitat structure contributes to the scale-dependent processes of community assembly across the dendritic metacommunity.
High-resolution structures of multiple 5-HT3AR-setron complexes reveal a novel mechanism of competitive inhibition.

Serotonin is perhaps best known as a chemical messenger in the brain, where it regulates mood, appetite and sleep. But as a hormone, serotonin works in other parts of the body too. Serotonin is predominantly made in the gut, where it binds receptor proteins that help to regulate the movement of substances through the gastrointestinal tract, aiding digestion. However, a surge in serotonin release in the gut induces vomiting and nausea, which commonly happens as a side effect of treating cancer with radiotherapy and chemotherapy. Anti-nausea drugs used to manage and prevent the severe nausea and vomiting experienced by cancer patients are therefore designed to target serotonin receptors in the gut. These drugs, called setrons, work by binding to serotonin receptors before serotonin does, essentially neutralising the effect of any surplus serotonin. Although they generally target serotonin receptors in the same way, some setrons are more efficient than others and can provide longer lasting relief. Clarifying exactly how each drug interacts with its target receptor might help to explain their differential effects. Basak et al. used a technique called cryo-electron microscopy to examine the interactions between three common anti-nausea drugs (palonosetron, ondansetron and alosetron) and one type of serotonin receptor, 5-HT3AR. The experiments showed that each drug changed the shape of 5-HT3AR, thereby inhibiting its activity to varying degrees. Further analysis identified a distinct ‘interaction fingerprint’ for the three setron drugs studied, showing which of the receptors’ subunits each drug binds to. Simulations of their interactions also showed that water molecules play a crucial role in the process, exposing the binding pocket on the receptor’s surface where the drugs attach. This work provides a structural blueprint of the interactions between anti-nausea drugs and serotonin receptors. The structures could guide the development of new and improved therapies to treat nausea and vomiting brought on by cancer treatments.
“Small step” remodeling and counterexamples for weighted estimates with arbitrarily “smooth” weights

Abstract For an A p weight w the norm of the Hilbert Transform in L p ( w ) , 1 p ∞ is estimated by [ w ] A p s , where [ w ] A p is the A p characteristic of the weight w and s = max ⁡ ( 1 , 1 / ( p − 1 ) ) ; as simple examples with power weights show, these estimates are sharp. A natural question to ask, is whether it is possible to improve the exponent s in the above estimate if one replaces the A p characteristic by its “fattened” version, where the averages are replaced by Poisson-like averages. For power weights (for example with p = 2 and Poisson averages) one can see that there is indeed an improvement in the exponent: but is it true for general weights? In this paper we show that the optimal exponent s remains the same by constructing counterexamples for arbitrarily “smooth” weights (in the sense that the doubling constant is arbitrarily close to 2), so the “fattened” A p characteristic is equivalent to the classical one, and such that ‖ T ‖ L p ( w ) ∼ [ w ] A p s . We use the ideas from the unpublished manuscript by F. Nazarov disproving Sarason's conjecture. We start from simple classical counterexamples for dyadic models, and then by using what we call “small step construction” we transform them into examples with weights that are arbitrarily dyadically smooth. F. Nazarov had used Bellman function method to prove the existence of such examples, but our construction gives a way to get such examples from the standard dyadic ones. We then use a modification of “remodeling”, introduced by J. Bourgain and developed by F. Nazarov, to get from examples for dyadic models to examples for the Hilbert transform. As an added bonus, we present a proof that the L p analog of Sarason's conjecture is false for all p, 1 p ∞ .
An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

Purpose Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)–based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. Methods and Materials The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners’ experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. Results All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. Conclusions We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.
Drosophila PDGF/VEGF signaling from muscles to hepatocyte-like cells protects against obesity

PDGF/VEGF ligands regulate a plethora of biological processes in multicellular organisms via autocrine, paracrine, and endocrine mechanisms. We investigated organ-specific metabolic roles of Drosophila PDGF/VEGF-like factors (Pvfs). We combine genetic approaches and single-nuclei sequencing to demonstrate that muscle-derived Pvf1 signals to the Drosophila hepatocyte-like cells/oenocytes to suppress lipid synthesis by activating the Pi3K/Akt1/TOR signaling cascade in the oenocytes. Functionally, this signaling axis regulates expansion of adipose tissue lipid stores in newly eclosed flies. Flies emerge after pupation with limited adipose tissue lipid stores and lipid level is progressively accumulated via lipid synthesis. We find that adult muscle-specific expression of pvf1 increases rapidly during this stage and that muscle-to-oenocyte Pvf1 signaling inhibits expansion of adipose tissue lipid stores as the process reaches completion. Our findings provide the first evidence in a metazoan of a PDGF/VEGF ligand acting as a myokine that regulates systemic lipid homeostasis by activating TOR in hepatocyte-like cells.
Constraints on the Optical Depth of Galaxy Groups and Clusters

Future data from galaxy redshift surveys, combined with high-resolutions maps of the cosmic microwave background, will enable measurements of the pairwise kinematic Sunyaev-Zel'dovich (kSZ) signal with unprecedented statistical significance. This signal probes the matter-velocity correlation function, scaled by the average optical depth ($\tau$) of the galaxy groups and clusters in the sample, and is thus of fundamental importance for cosmology. However, in order to translate pairwise kSZ measurements into cosmological constraints, external constraints on $\tau$ are necessary. In this work, we present a new model for the intra-cluster medium, which takes into account star-formation, feedback, non-thermal pressure, and gas cooling. Our semi-analytic model is computationally efficient and can reproduce results of recent hydrodynamical simulations of galaxy cluster formation. By calibrating the model using recent X-ray measurements of gas density profiles of clusters and $M_{\mathrm{gas}}-M$ relations of groups and clusters, we show that the integrated $\tau$ is constrained to better than 6% (with a confidence level of 95%), which sets the tightest constraints on the external prior on the optical depth of groups and clusters reported in the literature to date. Our strong prior on $\tau$ should significantly improve cosmological constraints derived from future pairwise kSZ measurements and shed new insights into the nature of dark energy, modified gravity, and neutrino mass.
Weak hamiltonian Wilson Coefficients from Lattice QCD

n this work we present a calculation of the Wilson Coefficients C 1 and C 2 of the Effective Weak Hamiltonian to all-orders in α s , using lattice simulations. Given the current availability of lattice spacings we restrict our calculation to unphysically light W bosons around 2 GeV and we study the systematic uncertainties of the two Wilson Coefficients.
Mud, Microbes, and Macrofauna: Seasonal Dynamics of the Iron Biogeochemical Cycle in an Intertidal Mudflat

Microorganisms and burrowing animals exert a pronounced impact on the cycling of redox sensitive metals in coastal sediments both spatially and temporally. In this study, we monitored the spatiotemporal dynamics of porewater and highly reactive solid phase iron with the corresponding prokaryotic and eukaryotic sedimentary microbial communities over one annual cycle from November 2015 to November 2016 in a bioturbated intertidal mudflat. Continuous and seasonally variable pools of both porewater Fe(II) and highly reactive iron (FeHR) were observed throughout the seasons with significant increases of Fe(II) and FeHR in response to increased sediment temperature in summer months. Maximum concentrations of Fe(II) and FeHR were predominantly confined to the upper 5 cm of sediment throughout the year. Iron-oxidizing and -reducing microorganisms were present and stable temporally, and exhibited a depth-dependent stratification likely due to availability of Fe(II) and FeHR pools, respectively. Zetaproteobacteria, presumptive lithotrophic iron-oxidizing bacteria, were present at abundances around 0.5 – 1% in the top five cm of sediment with decling abundance with depth. As a whole the microbial community was relatively stable across the seasons, and showed strongest separation with depth, probably driven by changes in oxygen availability and organic matter. The Deltaproteobacteria, principally taxa known to be associated with sulfur and iron cycling, were prevalent, especially at >5 cm depth. Gammaproteobacteria and Bacteroidetes were also abundant, with putatively aerobic members especially prevalent in the cm of the sediment. The relative abundance of diatoms, estimated from abundance of 18S rRNA gene counts, showed evidence of a seasonal signal possibly tied to spring and fall blooms. Overall, analysis of phytoplankton found significant abundance at depth, likely due to the feeding and bio-mixing activity of marine worms. Macro-, and meiofauna, consistent with expected taxa, were detected throughout the year via 18S gene counts, and showed some seasonal variations that may influence sedimentary iron transformations by active microbial grazing. In summary, this analysis revealed relatively consistent temporal and spatial trends in iron geochemistry and microbial and macrobial community composition, while also indicating a complex dynamic of microbial and macrobial interactions are responsible for maintaining these processes.
Torus solutions to the Weierstrass-Enneper representation of surfaces

In this paper, we present a torus solution to the generalized Weierstrass-Enneper representation of surfaces in R4. The key analytical technique will be Bloch wave functions with complex wave vectors. We will also discuss some possible uses of these solutions which derive from their explicit nature, such as Dehn surgery and the physics of exotic smooth structure.In this paper, we present a torus solution to the generalized Weierstrass-Enneper representation of surfaces in R4. The key analytical technique will be Bloch wave functions with complex wave vectors. We will also discuss some possible uses of these solutions which derive from their explicit nature, such as Dehn surgery and the physics of exotic smooth structure.
Applying a resources framework to analysis of the Force and Motion Conceptual Evaluation

We suggest one redefinition of common clusters of questions used to analyze student responses on the Force and Motion Conceptual Evaluation. Our goal is to propose a methodology that moves beyond an analysis of student learning defined by correct responses, either on the overall test or on clusters of questions defined solely by content. We use the resources framework theory of learning to define clusters within this experimental test that was designed without the resources framework in mind. We take special note of the contextual and representational dependence of questions with seemingly similar physics content. We analyze clusters in ways that allow the most common incorrect answers to give as much, or more, information as the correctness of responses in that cluster. We show that false positives can be found, especially on questions dealing with Newton's third law. We apply our clustering to a small set of data to illustrate the value of comparing students' incorrect responses which are otherwise identical on a correct or incorrect analysis. Our work provides a connection between theory and experiment in the area of survey design and the resources framework.
Cantor series constructions of sets of normal numbers

Let $Q=(q_n)_{n=1}^{\infty}$ be a sequence of integers greater than or equal to 2. We say that a real number $x$ in $[0,1)$ is {\it $Q$-distribution normal} if the sequence $(q_1q_2... q_n x)_{n=1}^{\infty}$ is uniformly distributed mod 1. In \cite{Lafer}, P. Lafer asked for a construction of a $Q$-distribution normal number for an arbitrary $Q$. Under a mild condition on $Q$, we construct a set $\Theta_Q$ of $Q$-distribution normal numbers. This set is perfect and nowhere dense. Additionally, given any $\alpha$ in $[0,1]$, we provide an explicit example of a sequence $Q$ such that the Hausdorff dimension of $\Theta_Q$ is equal to $\alpha$. Under a certain growth condition on $q_n$, we provide a discrepancy estimate that holds for every $x$ in $\Theta_Q$.
Non-Hermitian topological metamaterials with odd elasticity

This work constructs non-Hermitian topological mechanics in one-dimensional and two-dimensional lattices consisting of mass points connected by meta-beams that lead to odd elasticity. The authors propose a definition of the Berry phase in non-Hermitian systems to characterize the lattice topology. Their results show that the associated eigenfrequencies of topological boundary modes are complex, meaning that the excitations could exponentially grow in time even in the damped regime.
Proof of the Arnold chord conjecture in three dimensions, II

In "Proof of the Arnold chord conjecture in three dimensions I", we deduced the Arnold chord conjecture in three dimensions from another result, which asserts that an exact symplectic cobordism between contact three-manifolds induces a map on (filtered) embedded contact homology satisfying certain axioms. The present paper proves the latter result, thus completing the proof of the three-dimensional chord conjecture. We also prove that filtered embedded contact homology does not depend on the choice of almost complex structure used to define it.
Rotations of small, inertialess triaxial ellipsoids in isotropic turbulence

The statistics of rotational motion of small, inertialess triaxial ellipsoids are computed along Lagrangian trajectories extracted from direct numerical simulations of homogeneous isotropic turbulence. The total particle angular velocity and its components along the three principal axes of the particle are considered, expanding on the results of Chevillard & Meneveau (J. Fluid Mech., vol. 737, 2013, pp. 571–596) who showed results of the rotation rate of the particle’s principal axes. The variance of the particle angular velocity, referred to as the particle enstrophy, is found to increase as particles become elongated, regardless of whether they are axisymmetric. This trend is explained by considering the contributions of vorticity and strain rate to particle rotation. It is found that the majority of particle enstrophy is due to fluid vorticity. Strain-rate-induced rotations, which are sensitive to shape, are mostly cancelled by strain–vorticity interactions. The remainder of the strain-rate-induced rotations are responsible for weak variations in particle enstrophy. For particles of all shapes, the majority of the enstrophy is in rotations about the longest axis, which is due to alignment between the longest axis and fluid vorticity. The integral time scale for particle angular velocities about different axes reveals that rotations are most persistent about the longest axis, but that a full revolution is rare.
Nonnegative Ricci curvature, stability at infinity and finite generation of fundamental groups

We study the fundamental group of an open n–manifold M of nonnegative Ricci curvature. We show that if there is an integer k such that any tangent cone at infinity of the Riemannian universal cover of M is a metric cone whose maximal Euclidean factor has dimension k, then π1(M) is finitely generated. In particular, this confirms the Milnor conjecture for a manifold whose universal cover has Euclidean volume growth and a unique tangent cone at infinity.
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs. As reducing precision is one of the most effective knobs for boosting training time/energy efficiency, there has been a growing interest in low-precision DNN training. In this paper, we explore from an orthogonal direction: how to fractionally squeeze out more training cost savings from the most redundant bit level, progressively along the training trajectory and dynamically per input. Specifically, we propose FracTrain that integrates (i) progressive fractional quantization which gradually increases the precision of activations, weights, and gradients that will not reach the precision of SOTA static quantized DNN training until the final training stage, and (ii) dynamic fractional quantization which assigns precisions to both the activations and gradients of each layer in an input-adaptive manner, for only "fractionally" updating layer parameters. Extensive simulations and ablation studies (six models, four datasets, and three training settings including standard, adaptation, and fine-tuning) validate the effectiveness of FracTrain in reducing computational cost and hardware-quantified energy/latency of DNN training while achieving a comparable or better (-0.12%~+1.87%) accuracy. For example, when training ResNet-74 on CIFAR-10, FracTrain achieves 77.6% and 53.5% computational cost and training latency savings, respectively, compared with the best SOTA baseline, while achieving a comparable (-0.07%) accuracy. Our codes are available at: this https URL.
Soft elastic surfaces as a platform for particle self-assembly

We perform numerical simulations to study self-assembly of nanoparticles mediated by an elastic planar surface. We show how the nontrivial elastic response to deformations of these surfaces leads to anisotropic interactions between the particles resulting in aggregates having different geometrical features. The morphology of the patterns can be controlled by the mechanical properties of the surface and the strength of the particle adhesion. We use simple scaling arguments to understand the formation of the different structures, and we show how the adhering particles can cause the underlying elastic substrate to wrinkle if two of its opposite edges are clamped. Finally, we discuss the implications of our results and suggest how elastic surfaces could be used in nanofabrication.
Excited states of a static dilute spherical Bose condensate in a trap

The Bogoliubov approximation is use to study the excited states of a dilute gas ofN atomic bosons trapped in an isotropic harmonic potential characterized by a frequency ω0 and an oscillator lengthd0=\(\sqrt {\hbar /m_{\omega 0} } \). The self-consistent static Bose condensate has macroscopic occupation numberN0>>1, with nonuniform spherical condensate densityn0(r); by assumption, the deletion of the condensate is small (N≡N-N0 0 that provides an upper bound for the lowest eigenvalue ωl and an estimate for the corresponding zero-temperature occupation numberN′l; both expression closely resemble those for a uniform bulk Bose condensate.
An Upper Limit on the Granularity of the Local Stellar Halo

I use the statistical properties of 4588 nearby halo stars to show that if the local stellar halo is composed of cold streams from disrupted dwarf satellites as is predicted by popular cosmologies, then at 95% confidence there are at least 400 such streams. Moreover, no stream can contain more than 5.0% of the local stars, while average streams must contain less than 0.25%.
Quotient singularities, eta invariants, and self-dual metrics

There are three main components to this article: (i) A formula for the eta invariant of the signature complex for any finite subgroup of ${\rm{SO}}(4)$ acting freely on $S^3$ is given. An application of this is a non-existence result for Ricci-flat ALE metrics on certain spaces. (ii) A formula for the orbifold correction term that arises in the index of the self-dual deformation complex is proved for all finite subgroups of ${\rm{SO}}(4)$ which act freely on $S^3$. Some applications of this formula to the realm of self-dual and scalar-flat K\"ahler metrics are also discussed. (iii) Two infinite families of scalar-flat anti-self-dual ALE spaces with groups at infinity not contained in ${\rm{U}}(2)$ are constructed. Using these spaces, new examples of self-dual metrics on $n \# \mathbb{CP}^2$ are obtained for $n \geq 3$.
Directional interactions and cooperativity between mechanosensitive membrane proteins

While modern structural biology has provided us with a rich and diverse picture of membrane proteins, the biological function of membrane proteins is often influenced by the mechanical properties of the surrounding lipid bilayer. Here we explore the relation between the shape of membrane proteins and the cooperative function of membrane proteins induced by membrane-mediated elastic interactions. For the experimental model system of mechanosensitive ion channels we find that the sign and strength of elastic interactions depend on the protein shape, yielding distinct cooperative gating curves for distinct protein orientations. Our approach predicts how directional elastic interactions affect the molecular structure, organization, and biological function of proteins in crowded membranes.
Coherently displaced oscillator quantum states of a single trapped atom

Coherently displaced number states of a harmonically bound ion can be coupled to two internal states of the ion by a laser-induced motional sideband interaction. The internal states can subsequently be read out in a projective measurement via state-dependent fluorescence, with near-unit fidelity. This leads to a rich set of line shapes when recording the internal-state excitation probability after a sideband excitation, as a function of the frequency detuning of the displacement drive with respect to the ion's motional frequency. We precisely characterize the coherent displacement based on the resulting line shapes, which exhibit sharp features that are useful for oscillator frequency determination from the single quantum regime up to very large coherent states with average occupation numbers of several hundred. We also introduce a technique based on multiple coherent displacements and free precession for characterizing noise on the trapping potential in the frequency range of 500 Hz–400 kHz. Signals from the ion are directly used to find and eliminate sources of technical noise in this typically unaccessed part of the spectrum.
An apparent paradox concerning the field of an ideal dipole

The electric or magnetic field of an ideal dipole is known to have a Dirac delta function at the origin. The usual textbook derivation of this delta function is rather ad hoc and cannot be used to calculate the delta-function structure for higher multipole moments. Moreover, a naive application of Gauss's law to the ideal dipole field appears to give an incorrect expression for the dipole's effective charge density. We derive a general result for the delta-function structure at the origin of an arbitrary ideal multipole field without using any advanced techniques from distribution theory. We find that the divergence of a singular vector field can contain a derivative of a Dirac delta function even if the field itself does not contain a delta function. We also argue that a physical interpretation of the delta function in the dipole field previously given in the literature is perhaps misleading and may require clarification. Both the explanation of and the resolution to this 'paradox' should be accessible to someone who has taken a graduate- or advanced undergraduate-level course in classical electrodynamics.
Automated alignment of a reconfigurable optical system using focal-plane sensing and Kalman filtering.

Automation of alignment tasks can provide improved efficiency and greatly increase the flexibility of an optical system. Current optical systems with automated alignment capabilities are typically designed to include a dedicated wavefront sensor. Here, we demonstrate a self-aligning method for a reconfigurable system using only focal plane images. We define a two lens optical system with 8 degrees of freedom. Images are simulated given misalignment parameters using ZEMAX software. We perform a principal component analysis on the simulated data set to obtain Karhunen–Loeve modes, which form the basis set whose weights are the system measurements. A model function, which maps the state to the measurement, is learned using nonlinear least-squares fitting and serves as the measurement function for the nonlinear estimator (extended and unscented Kalman filters) used to calculate control inputs to align the system. We present and discuss simulated and experimental results of the full system in operation.
The shortened KK spectrum of IIB supergravity on Y p,q

We examine the shortened KK spectrum of IIB supergravity compactified on Y p,q and conjecture that the spectrum we have obtained is complete. The (untwisted) shortened spectrum on S 5 / $ \mathbb{Z} $ 2p and on T 1,1 / $ \mathbb{Z} $ p are obtained as special cases when p = q and q = 0, respectively. Knowledge of the shortened spectrum allows us to compute the superconformal index of these theories and to find agreement with earlier calculations from the dual field theories. We also employ the shortened spectrum to perform a 1/N 2 test of AdS/CFT by holographically reproducing the difference of the central charges, c − a = p/8, of the dual CFTs.
Resource-enhanced neural model for event argument extraction

Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data. For (2), we use Transformer that uses dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE 2005 benchmark show that our approach achieves a new state-of-the-art.
Critical Phenomena and Kibble-Zurek Scaling in the Long-Range Quantum Ising Chain

We investigate an extension of the quantum Ising model in one spatial dimension including long-range $1 / r^{\alpha}$ interactions in its statics and dynamics with possible applications from heteronuclear polar molecules in optical lattices to trapped ions described by two-state spin systems. We introduce the statics of the system via both numerical techniques with finite size and infinite size matrix product states and a theoretical approaches using a truncated Jordan-Wigner transformation for the ferromagnetic and antiferromagnetic case and show that finite size effects have a crucial role shifting the quantum critical point of the external field by fifteen percent between thirty-two and around five-hundred spins. We numerically study the Kibble-Zurek hypothesis in the long-range quantum Ising model with Matrix Product States. A linear quench of the external field through the quantum critical point yields a power-law scaling of the defect density as a function of the total quench time. For example, the increase of the defect density is slower for longer-range models and the critical exponent changes by twenty-five per cent. Our study emphasizes the importance of such long-range interactions in statics and dynamics that could point to similar phenomena in a different setup of dynamical systems or for other models.
Isolating electrons on superfluid helium

Electrons floating on the surface of superfluid helium have been suggested as promising mobile spin quantum bits (qubits). Transferring electrons extremely efficiently in a narrow channel structure with underlying gates has been demonstrated, showing no transfer error while clocking 109 pixels in a 3-phase charge coupled device (CCD). While on average, one electron per channel was clocked, it is desirable to reliably obtain a single electron per channel. We have designed an electron turnstile consisting of a narrow (0.8μm) channel and narrow underlying gates (0.5μm) operating across seventy-eight parallel channels. Initially, we find that more than one electron can be held above the small gates. Underlying gates in the turnstile region allow us to repeatedly split these electron packets. Results show a plateau in the electron signal as a function of the applied gate voltages, indicating quantization of the number of electrons per pixel, simultaneously across the seventy-eight parallel channels.
Knot Floer Homology and Khovanov-Rozansky Homology for Singular Links

The (untwisted) oriented cube of resolutions for knot Floer homology assigns a complex $C_{F}(S)$ to a singular resolution $S$ of a knot $K$. Manolescu conjectured that when $S$ is in braid position, the homology $H_{*}(C_{F}(S))$ is isomorphic to the HOMFLY-PT homology of $S$. Together with a naturality condition on the induced edge maps, this conjecture would prove the spectral sequence from HOMFLY-PT homology to knot Floer homology. Using a basepoint filtration on $C_{F}(S)$, a recursion formula for HOMFLY-PT homology, and additional $sl_{n}$-like differentials on $C_{F}(S)$, we prove this conjecture.
Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information , such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.
Invariant Manifolds of Traveling Waves of the 3D Gross–Pitaevskii Equation in the Energy Space

We study the local dynamics near general unstable traveling waves of the 3D Gross–Pitaevskii equation in the energy space by constructing smooth local invariant center-stable, center-unstable and center manifolds. We also prove that (i) the center unstable manifold attracts nearby orbits exponentially before they go away from the traveling waves along the center or unstable directions and (ii) if an initial data is not on the center-stable manifolds, then the forward orbit leaves traveling waves exponentially fast. Furthermore, under an additional non-degeneracy assumption, we show the orbital stability of the travelingwaves on the centermanifolds,which also implies the uniqueness of the local invariant manifolds. Our method based on a geometric bundle coordinate system should work for a general class of Hamiltonian PDEs.
Non-Equilibrium Thermodynamics and Topology of Currents

In many experimental situations, a physical system undergoes stochastic evolution which may be described via random maps between two compact spaces. In the current work, we study the applicability of large deviations theory to time-averaged quantities which describe such stochastic maps, in particular time-averaged currents and density functionals. We derive the large deviations principle for these quantities, as well as for global topological currents, and formulate variational, thermodynamic relations to establish large deviation properties of the topological currents. We illustrate the theory with a nontrivial example of a Heisenberg spin-chain with a topological driving of the Wess-Zumino type. The Cramer functional of the topological current is found explicitly in the instanton gas regime for the spin-chain model in the weak-noise limit. In the context of the Morse theory, we discuss a general reduction of continuous stochastic models with weak noise to effective Markov chains describing transitions between stable fixed points.