cssi/SciSciGPT-SciSciNet huggingface dataset:
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication

Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, and this is the so-called 'cold-start' problem. Observing that in a multi-agent network the learning tasks across different agents often share some model similarity, we ask the following fundamental question: "Is it possible to accelerate the online meta-learning across agents via limited communication and if yes how much benefit can be achieved?" To answer this question, we propose a multi-agent online meta-learning framework and cast it as an equivalent two-level nested online convex optimization (OCO) problem. By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed network-level OCO for meta-model updates via limited communication, which however is not well understood. To tackle this challenge, we devise a distributed online gradient descent algorithm with gradient tracking where each agent tracks the global gradient using only one communication step with its neighbors per iteration, and it results in an average regret O(T/N) per agent, indicating that a factor of 1/N speedup over the optimal single-agent regret O(T) after T iterations, where N is the number of agents. Building on this sharp performance speedup, we next develop a multi-agent online meta-learning algorithm and show that it can achieve the optimal task-average regret at a faster rate of O(1 N/T) via limited communication, compared to single-agent online meta-learning. Extensive experiments corroborate the theoretic results.
Characterization of an operon required for growth on cellobiose in Clostridioides difficile

Cellobiose metabolism is linked to the virulence properties in numerous bacterial pathogens. Here, we characterized a putative cellobiose PTS operon of Clostridiodes difficile to investigate the role of cellobiose metabolism in C. difficile pathogenesis. Our gene knockout experiments demonstrated that the putative cellobiose operon enables uptake of cellobiose into C. difficile and allows growth when cellobiose is provided as the sole carbon source in minimal medium. Additionally, using reporter gene fusion assays and DNA pulldown experiments, we show that its transcription is regulated by CelR, a novel transcriptional repressor protein, which directly binds to the upstream region of the cellobiose operon to control its expression. We have also identified cellobiose metabolism to play a significant role in C. difficile physiology as observed by the reduction of sporulation efficiency when cellobiose uptake was compromised in the mutant strain. In corroboration to in vitro study findings, our in vivo hamster challenge experiment showed a significant reduction of pathogenicity by the cellobiose mutant strain in both the primary and the recurrent infection model - substantiating the role of cellobiose metabolism in C. difficile pathogenesis.
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning

GPS technology has revolutionized the way we localize and navigate outdoors. However, the poor reception of GPS signals in buildings makes it unsuitable for indoor localization. WiFi fingerprinting-based indoor localization is one of the most promising ways to meet this demand. Unfortunately, most work in the domain fails to resolve challenges associated with deployability on resource-limited embedded devices. In this work, we propose a compression-aware and high-accuracy deep learning framework called CHISEL that outperforms the best-known works in the area while maintaining localization robustness on embedded devices.
Low Complexity Sequential Search With Size-Dependent Measurement Noise

This paper considers a target localization problem where at any given time an agent can choose a region to query for the presence of the target in that region. The measurement noise is assumed to be increasing with the size of the query region the agent chooses. Motivated by practical applications such as initial beam alignment in array processing, heavy hitter detection in networking, and visual search in robotics, we consider practically important complexity constraints/metrics: time complexity , computational and memory complexity , and the complexity of possible query sets in terms of geometry and cardinality. Two novel search strategy, $dyaPM$ and $hiePM$ , are proposed. Pertinent to the practicality of our solutions, $dyaPM$ and $hiePM$ are of a connected query geometry (i.e. query set is always a connected set) implemented with low computational and memory complexity. Additionally, $hiePM$ has a hierarchical structure and, hence, a further reduction in the cardinality of possible query sets, making $hiePM$ practically suitable for applications such as beamforming in array processing where memory limitations favors a small and predefined query sets. Through a unified analysis with Extrinsic Jensen Shannon (EJS) Divergence, $dyaPM$ is shown to be asymptotically optimal in search time complexity (asymptotic in both resolution (rate) and error (reliability)). On the other hand, $hiePM$ is shown to be near-optimal in rate. In addition, both $hiePM$ and $dyaPM$ are shown to outperform prior work in the non-asymptotic regime.
Book-ahead & supply management for ridesourcing platforms

Abstract Ridesourcing platforms recently introduced the “schedule a ride” service where passengers may reserve (book-ahead) a ride in advance of their trip. Reservations give platforms precise information that describes the start time and location of anticipated future trips; in turn, platforms can use this information to adjust the availability and spatial distribution of the driver supply. In this article, we propose a framework for modeling/analyzing reservations in time-varying stochastic ridesourcing systems. We consider that the driver supply is distributed over a network of geographic regions and that book-ahead rides have reach time priority over non-reserved rides. First, we propose a state-dependent admission control policy that assigns drivers to passengers; this policy ensures that the reach time service requirement would be attained for book-ahead rides. Second, given the admission control policy and reservations information in each region, we predict the “target” number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides. Third, we propose a reactive dispatching/rebalancing mechanism that determines the adjustments to the driver supply that are needed to maintain the targets across regions. For a specific reach time quality of service, simulation results using data from Lyft rides in Manhattan exhibit how the number of idle drivers decreases with the fraction of book-ahead rides. We also observe that the non-stationary demand (ride request) rate varies significantly across time; this rapid variation further illustrates that time-dependent models are needed for operational analysis of ridesourcing systems.
Causality, unitarity and symmetry in effective field theory

Sum rules in effective field theories, predicated upon causality, place restrictions on scattering amplitudes mediated by effective contact interactions. Through unitarity of the S-matrix, these imply that the size of higher dimensional corrections to transition amplitudes between different states is bounded by the strength of their contributions to elastic forward scattering processes. This places fundamental limits on the extent to which hypothetical symmetries can be broken by effective interactions. All analysis is for dimension 8 operators in the forward limit. Included is a thorough derivation of all positivity bounds for a chiral fermion in SU(2) and SU(3) global symmetry representations resembling those of the Standard Model, general bounds on flavour violation, new bounds for interactions between particles of different spin, inclusion of loops of dimension 6 operators and illustration of the resulting strengthening of positivity bounds over tree-level expectations, a catalogue of supersymmetric effective interactions up to mass dimension 8 and 4 legs and the demonstration that supersymmetry unifies the positivity theorems as well as the new bounds.
Large genus asymptotics for intersection numbers and principal strata volumes of quadratic differentials

In this paper we analyze the large genus asymptotics for intersection numbers between $$\psi $$ -classes, also called correlators, on the moduli space of stable curves. Our proofs proceed through a combinatorial analysis of the recursive relations (Virasoro constraints) that uniquely determine these correlators, together with a comparison between the coefficients in these relations with the jump probabilities of a certain asymmetric simple random walk. As an application of this result, we provide the large genus limits for Masur–Veech volumes and area Siegel–Veech constants associated with principal strata in the moduli space of quadratic differentials. These confirm predictions of Delecroix–Goujard–Zograf–Zorich from 2019.
Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties

Peg-in-hole assembly is a challenging contact-rich manipulation task. There is no general solution to identify the relative position and orientation between the peg and the hole. In this paper, we propose a novel method to classify the contact poses based on a sequence of contact measurements. When the peg contacts the hole with pose uncertainties, a tilt-then-rotate strategy is applied, and the contacts are measured as a group of patterns to encode the contact pose. A convolutional neural network (CNN) is trained to classify the contact poses according to the patterns. In the end, an admittance controller guides the peg towards the error direction and finishes the peg-in-hole assembly. Simulations and experiments are provided to show that the proposed method can be applied to the peg-in-hole assembly of different geometries. We also demonstrate the ability to alleviate the sim-to-real gap.
Implementation of Simultaneous Multi-Streaming of Live Solar Eclipse Video via 5.8 GHz AirMax

For the 2017 solar eclipse ballooning, we have developed a video payload that can simultaneously live-stream multiple videos via a single 5.8 GHz wireless link through a Ubiquiti Rocket M5 modem. In this paper, we describe our approach to multiplex multiple video streams into a single data stream that a 5.8 GHz wireless modem can transport to the ground station. Various key factors are described to properly configure the Raspberry Pis and optimize the transmission from an M5 modem on the video payload over the 5.8 GHz link while ensuring adequate range and acceptable video quality received at the ground station. A screenshot of the multi-video streaming is provided as an example to justify a successful operation of our video payload.
Is electromagnetic field momentum due to the flow of field energy

Abstract Momentum and energy conservation require electromagnetic field momentum and energy to be treated as physically real, even in static fields. This motivates the conjecture that field momentum might be due to the flow of a relativistic mass density (defined as energy density divided by the square of the speed of light). This article investigates the velocity of such a mass flow and finds a conflict between two different definitions of it, both of which originally seem plausible if the flow is to be taken as real. This investigation is careful to respect the transformation rules of special relativity throughout. The paper demonstrates that the consensus definition of the flow velocity of electromagnetic energy is inconsistent with the transformation rules of special relativity, and hence is incorrect. A replacement flow velocity is derived which is completely consistent with those transformation rules. The conclusion is that these conflicting definitions of flow velocity cannot be resolved in a way that is consistent with special relativity and also allows electromagnetic field momentum density to be the result of relativistic mass flow. Though real, field momentum density cannot be explained as the flow of a relativistic mass density. As a byproduct of the study, it is also shown that there is a comoving system in which the electromagnetic energy-momentum tensor is reduced to a simple diagonal form, with two of its diagonal elements equal to the energy density and the other two diagonal elements equal to plus and minus a single parameter derived from the electromagnetic field values, a result that places constraints on possible fluid models of electromagnetism.
"Why wouldn't someone think of democracy as a target?": Security practices & challenges of people involved with U.S. political campaigns

People who are involved with political campaigns face increased digital security threats from well-funded, sophisticated attackers, especially nation-states. Improving political campaign security is a vital part of protecting democracy. To identify campaign security issues, we conducted qualitative research with 28 participants across the U.S. political spectrum to understand the digital security practices, challenges, and perceptions of people involved in campaigns. A main, overarching finding is that a unique combination of threats, constraints, and work culture lead people involved with political campaigns to use technologies from across platforms and domains in ways that leave them--and democracy--vulnerable to security attacks. Sensitive data was kept in a plethora of personal and work accounts, with ad hoc adoption of strong passwords, two-factor authentication, encryption, and access controls. No individual company, committee, organization, campaign, or academic institution can solve the identified problems on their own. To this end, we provide an initial understanding of this complex problem space and recommendations for how a diverse group of experts can begin working together to improve security for political campaigns.
Capacity Rationing in Primary Care: Provider Availability Shocks and Channel Diversion

We study capacity rationing by servers facing differentiated customer classes using data from the Veterans Health Administration, which is the largest integrated healthcare system in the U.S. Using...
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights

Machine learning (ML) models are widely used in many important domains. For efficiently processing these computational- and memory-intensive applications, tensors of these overparameterized models are compressed by leveraging sparsity, size reduction, and quantization of tensors. Unstructured sparsity and tensors with varying dimensions yield irregular computation, communication, and memory access patterns; processing them on hardware accelerators in a conventional manner does not inherently leverage acceleration opportunities. This article provides a comprehensive survey on the efficient execution of sparse and irregular tensor computations of ML models on hardware accelerators. In particular, it discusses enhancement modules in the architecture design and the software support, categorizes different hardware designs and acceleration techniques, analyzes them in terms of hardware and execution costs, analyzes achievable accelerations for recent DNNs, and highlights further opportunities in terms of hardware/software/model codesign optimizations (inter/intramodule). The takeaways from this article include the following: understanding the key challenges in accelerating sparse, irregular shaped, and quantized tensors; understanding enhancements in accelerator systems for supporting their efficient computations; analyzing tradeoffs in opting for a specific design choice for encoding, storing, extracting, communicating, computing, and load-balancing the nonzeros; understanding how structured sparsity can improve storage efficiency and balance computations; understanding how to compile and map models with sparse tensors on the accelerators; and understanding recent design trends for efficient accelerations and further opportunities.
TensorBNN: Bayesian inference for neural networks using TensorFlow

Abstract TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models . The posterior density of neural network model parameters is represented as a point cloud sampled using Hamiltonian Monte Carlo . The TensorBNN package leverages TensorFlow 's architecture and its ability to use modern graphics processing units in both the training and prediction stages.
Minimal group determinants for dicyclic groups

We determine the minimal nontrivial integer group determinant for the dicyclic group of order 4n when n is odd. We also discuss the set of all integer group determinants for the dicyclic groups of order 4p.
Exploring Tidal Obliquity Variations with SMERCURY-T

We introduce our new code, SMERCURY-T, which is based on existing codes SMERCURY (Lissauer et al. 2012) and Mercury-T (Bolmont et al. 2015). The result is a mixed-variable symplectic N-body integrator that can compute the orbital and spin evolution of a planet within a multi-planet system under the influence of tidal spin torques from its star. We validate our implementation by comparing our experimental results to that of a secular model. As we demonstrate in a series of experiments, SMERCURY-T allows for the study of secular spin-orbit resonance crossings and captures for planets within complex multi-planet systems. These processes can drive a planet's spin state to evolve along vastly different pathways on its road toward tidal equilibrium, as tidal spin torques dampen the planet's spin rate and evolve its obliquity. Additionally, we show the results of a scenario that exemplifies the crossing of a chaotic region that exists as the overlap of two spin-orbit resonances. The test planet experiences violent and chaotic swings in its obliquity until its eventual escape from resonance as it tidally evolves. All of these processes are and have been important over the obliquity evolution of many bodies within the Solar System and beyond, and have implications for planetary climate and habitability. SMERCURY-T is a powerful and versatile tool that allows for further study of these phenomena.
Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment.

Background Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. Findings We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. Conclusions Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.
A Bayesian latent class mixture model with censoring for correlation analysis in antimicrobial resistance across populations.

BACKGROUND The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options. METHODS In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data. RESULTS Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations. CONCLUSIONS Our proposed approach has been shown to be accurate and superior to the commonly used naive estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.
Fabrication and surface treatment of electron-beam evaporated niobium for low-loss coplanar waveguide resonators

We characterize low-loss electron-beam evaporated niobium thin films deposited under ultra-high vacuum conditions. Slow deposition yields films with a high superconducting transition temperature ( 9.20±0.06 K) as well as a residual resistivity ratio of 4.8. We fabricate the films into coplanar waveguide resonators to extract the intrinsic loss due to the presence of two-level-system fluctuators using microwave measurements. For a coplanar waveguide resonator gap of 2 μm, the films exhibit filling-factor-adjusted two-level-system loss tangents as low as 1.5×10−7 with single-photon regime internal quality factors in excess of one million after removing native surface oxides of the niobium.
Is the Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator

Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health. Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach that relies solely on the trajectory of the leader robot for generating observations. We model the human using a redundant, partially-observable dynamical system and we infer the posture using a standard particle filter. We compare our approach with postures from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed approach successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard motion capture.
APNN-TC: accelerating arbitrary precision neural networks on ampere GPU tensor cores

Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activations) are usually restricted by limited precision support on GPUs (e.g., int1 and int4). To break such restrictions, we introduce the first Arbitrary Precision Neural Network framework (APNN-TC)1 to fully exploit quantization benefits on Ampere GPU Tensor Cores. Specifically, APNN-TC first incorporates a novel emulation algorithm to support arbitrary short bit-width computation with int1 compute primitives and XOR/AND Boolean operations. Second, APNN-TC integrates arbitrary precision layer designs to efficiently map our emulation algorithm to Tensor Cores with novel batching strategies and specialized memory organization. Third, APNN-TC embodies a novel arbitrary precision NN design to minimize memory access across layers and further improve performance. Extensive evaluations show that APNN-TC can achieve significant speedup over CUTLASS kernels and various NN models, such as ResNet and VGG.
New evidence from the Lyman-alpha forest concerning the formation of galaxies

A new type of survey for galaxies with z > 2 will be described. The idea is to search for the spectroscopic imprint that the Hi disc of a foreground galaxy leaves on radiation emitted by a background QSO; namely, a Lyman-$\alpha $ absorption line broadened by radiation damping. A continuing survey has revealed the presence of 15 damped L$\alpha $ lines with redshifts between 1.8 and 2.8 in the spectra of 68 QSOS. In comparison, no more than three discs with the properties of nearby galaxies should have been detected. Furthermore, the mean column density of the 15 absorbers, $\langle $N(Hi)$\rangle $ = 1.4 $\times $ 10$^{21}$ cm$^{-2}$, is much larger than expected for the outskirts of Hi discs. Both statistical and physical evidence has accumulated which suggests that the damped L$\alpha $ systems are a distinct population of absorbers with properties reminiscent of Hi discs. First, the Lyman-$\alpha $ absorption lines detected in the survey follow a frequency distribution of equivalent width that cannot be due to previously detected cloud populations. At large equivalent widths, where the damped L$\alpha $ lines occur, a new population of absorbers must exist. This damped population is therefore unrelated to clouds that are known to differ physically from galaxy discs. Secondly, detailed studies of the damped population reveal many of the properties shared by the Hi discs of galaxies. For example, (a) the absorption spectra due to ions of abundant elements are dominated by low ions such as C$^{+}$, Si$^{+}$, and Fe$^{+}$, instead of C$^{3+}$ and Si$^{3+}$, which are usually seen and (b) the recent detection of 21 cm absorption at z = 2.04 in one of the damped systems shows that the Hi is cold and that it has a low level of turbulence ($\sigma \approx $ 10 km s$^{-1}$). Another piece of evidence connecting the damped population with discs is that the cosmological mass density of the absorbers is characterized by a density parameter, $\Omega \approx $ 3 $\times $ 10$^{-3}$/h. This is comparable to the $\Omega $ due to luminous baryons. We suggest that this agreement is not coincidental, but rather reflects the fact that we have detected the progenitors of the baryon content of nearby galaxies. The discovery of the damped population has a number of implications for theories of galaxy formation. First, if the damped absorbers are identified with the normal population of galaxies, the Hi discs at z > 2 have radii R $\approx $ 3 R$_{\text{Ho}}$ (Holmberg). Because their redshift distribution is consistent with formation at z > 2.8, the production of large Hi discs from the collapse of protogalaxies must occur more rapidly (within less than 3 Ga) than predicted in many theories. Secondly, the collapse to discs of present size must occur in the galactic plane rather than from the halo.
Data-driven Learning of Nonlocal Models: from high-fidelity simulations to constitutive laws.

We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials. The kernel, including both its functional form and parameters, is derived so that when used in a nonlocal solver, it generates solutions that closely match high-fidelity data. The optimal kernel therefore acts as a homogenized nonlocal continuum model that accurately reproduces wave motion in a smaller-scale, more detailed model that can include multiple materials. We apply this technique to wave propagation within a heterogeneous bar with a periodic microstructure. Several one-dimensional numerical tests illustrate the accuracy of our algorithm. The optimal kernel is demonstrated to reproduce high-fidelity data for a composite material in applications that are substantially different from the problems used as training data.
In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays

Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-rays (LCXRs), to facilitate diagnoses by providing “second-opinions” in addition to a physician’s or radiologist’s interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to indirectly aid in infection containment, however generating a reliably labeled image dataset for a novel disease is not an easy feat, nor is it of highest priority when combating a global pandemic. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states for other lung pathologies; this study aims to leverage that body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Furthermore, this study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94% accuracy (Code available at https://github.com/ramnadime/COVID19_LCXR_multiclassification).
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties

Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.
End exclusion zones in strongly stretched, molten polymer brushes of arbitrary shape

Theories of strongly stretched polymer brushes, particularly the parabolic brush theory, are valuable for providing analytically tractable predictions for the thermodynamic behavior of surface-grafted polymers in a wide range of settings. However, the parabolic brush limit fails to describe polymers grafted to convex, curved substrates, such as the surfaces of spherical nanoparticles or the interfaces of strongly segregated block copolymers. It has been previously shown that strongly-stretched, curved brushes require a boundary layer devoid of free chain ends, requiring modifications of the theoretical analysis. While this "end exclusion zone" has been successfully incorporated into descriptions of brushes grafted onto the outer surfaces of cylinders and spheres, the behavior of brushes on surfaces of arbitrary curvature has not yet been studied. We present a formulation of the strong-stretching theory for molten brushes on surfaces of arbitrary curvature and identify four distinct regimes of interest for which brushes are predicted to possess end exclusion zones, notably including regimes of positive mean curvature but negative Gaussian curvature. Through numerical solutions of the strong-stretching brush equations, we report predicted scaling of the size of the end exclusion zone, the chain end distribution, the chain polarization, and the free energy of stretching with mean and Gaussian surface curvatures. Through these results, we present a comprehensive picture of how brush geometry influences the end exclusions zones and exact strong-stretching free energies, which can be applied, for example, to model the full spectrum of brush geometries encountered in block copolymer melt assembly.
Chebyshev expansion of spectral functions using restricted Boltzmann machines

Calculating the spectral function of two dimensional systems is arguably one of the most pressing challenges in modern computational condensed matter physics. While efficient techniques are available in lower dimensions, two dimensional systems present insurmountable hurdles, ranging from the sign problem in quantum Monte Carlo (MC), to the entanglement area law in tensor network based methods. We hereby present a variational approach based on a Chebyshev expansion of the spectral function and a neural network representation for the wave functions. The Chebyshev moments are obtained by recursively applying the Hamiltonian and projecting on the space of variational states using a modified natural gradient descent method. We compare this approach with a modified approximation of the spectral function which uses a Krylov subspace constructed from the "Chebyshev wave-functions". We present results for the one-dimensional and two-dimensional Heisenberg model on the square lattice, and compare to those obtained by other methods in the literature.
Statistical Thermodynamic Background

By an “equilibrium aggregate” is meant one whose formation involves physical attractive forces only (e.g., van der Waals and electrostatic interactions between protein molecules). This is the simplest case and is the subject of Part I of this book. Part II treats “steady-state aggregates.” This term refers to cases in which the monomers are enzyme molecules and a chemical reaction, catalyzed by the aggregated enzyme, accompanies or follows monomer attachment to the aggregate. Actin and microtubules provide examples of this, in which the chemical reactions are the hydrolysis of ATP or of GTP, which are bound to the respective monomers. Furthermore, in the case of microtubules, at least, it is possible for a microtubule end to spend most of its time in either of two distinct states or phases: the subunits at and near the end have GTP bound (not yet hydrolyzed) or these subunits have GDP bound (i.e., the GTP has been hydrolyzed). In-the first phase, the microtubule end is stable; in the second it is unstable. This two-phase behavior is novel and requires separate treatment (Chapter 8).
Cryo-Electron Microscopy of the Translational Apparatus: Experimental Evidence for the Paths of mRNA, tRNA, and the Polypeptide Chain

As a process that brings together, in close proximity, two linear structures of considerable length (mRNA and the nascent polypeptide chain) and large protein factors (EF-G and aminoacyl-tRNA·EF-Tu·GTP ternary complex), protein synthesis poses a logistic problem of traffic control: how to guarantee uninterrupted, high-precision performance without steric interference and entanglement of the various ligands. Since cryo-electron microscopy (cryo-EM) visualization provided the first detailed three-dimensional (3-D) images of the ribosome, much work has gone into the mapping of tRNA and elongation factors bound to the ribosome at various stages of the elongation cycle. A recent study of a 70S ribosome carrying a genetically inserted tRNA-like RNA fragment furnished a higher-resolution (17-A) map of the vacant ribosome, and the use of this new map in the subtraction produced a linear mass distribution covering the platform side segment of the mRNA path, as well as a mass hovering just at the entrance of the 30S subunit channel. tRNA bound to the ribosome has been directly visualized by 3-D cryo-EM in various tRNA-ribosome complexes. Visualization of the ribosome-bound tRNA is still a challenging task because the smallest dimension of the molecule is on the order of the resolution of cryo-EM and the occupancy of some tRNA binding states is intrinsically low.
Difficulties with the Scars Model of Quantum Localization

Numerical calculations studying bound eigenstates in chaotic regions of phase space, including those of the stadium billiard, are summarized. These calculations demonstrate that the scars of periodic orbit model is seriously flawed. An alternative picture of localization in chaotic regions is outlined.
Variable Selectivities of Acyl Coenzyme A:Monoacylglycerophosphate Acyltransferases in Rat Liver

Abstract The selectivity in vitro of microsomal acyl-CoA:monoacyl-sn-glycerol-3-phosphate acyltransferases was shown to be variable depending upon the incubation conditions. The availability of the phospholipid substrate can play an important role in regulating the observed selectivity of the enzymes giving higher selectivity at lower monoacyl-sn-glycerol-3-phosphate concentrations. Kinetic considerations support an idea that different catalytic sites may be involved when 1-acyl-sn-glycerol-3-phosphate is being acylated with different acyl-CoA's by rat liver microsomes. Thus, when the acylation occurred at very low 1-acyl-GP concentrations, palmitate and arachidonate tended to be excluded, although to a different degree, from the 2-position of diacyl-GP, whereas relatively nonselective acylation occurs at high 1-acyl-GP concentrations.
Cyclic polysilanes : XI. Reactivity of 1,2,3,4-tetra-t-butyltetramethylcyclotetrasilane: Stereospecific chlorodemethylation☆

Abstract Though highly strained, the four-silicon ring 1,2,3,4-tetra-t-butyltetramethylcyclotetrasilane, (t-BuMeSi) 4 (I), is stable in air and resists substitution. Chlorination generally occurs with ring cleavage, but chlorodemethylation of the intact ring is possible with HCl/AlCl 3 . Structures for some of the stereoisomeric mono- and di-chlorinated cyclotetrasilane products have been assigned based on their 1 H NMR Spectra. Stereospecific chlorodemethylation of the all- trans isomer of I with retention of configuration is indicated.
Community corrections professionals’ attitudes about sex offenders: is the CATSO applicable?

Little is known about how criminal justice officials perceive fairness, efficacy or scope of sex offender registration, community notification procedures, residency restrictions, and beliefs about sex offenders. The present study examines the utility of assessing community corrections professionals’ views of sex offenders using the community attitudes toward sex offenders scale. Evaluation of the applicability of this standardized scale for understanding community corrections professionals’ views reveals problems with the scale’s application.
The Emergence of a New Cultural Infrastructure: Lessons from Silicon Valley

Formal organizational structures and patterns of production and distribution of culture typical in most major cities emerged with, and are patterned after, Industrial Age thinking and Eurocentric cultural forms. These modernist, hierarchical, centralized, mass-production-oriented models-that separate art producer from consumer-face unprecedented challenges. In this article I argue that urban cultural infrastructures, and the institutions and organizing principles that comprise them, are being undermined and replaced. Research into the cultural infrastructure of California's Silicon Valley finds more decentralized, non-hierarchical, participatory, and culturally-diverse patterns. These stand out in comparison with nine other U.S. cites. This contemporary cradle of innovation and its cultural infrastructure similar forms of participant-generated self-expression, interaction, and self-organizing emerging from the region’s corporate cubicles and start-up garages. I call for more research on how urban cultural infrastructures are likely to evolve and suggest that Silicon Valley, and its core city of San Jose, offer some indications.
Neural network models for illusory contour perception

A physiologically motivated model of illusory contour perception is examined by simulating a neural network architecture that was tested with gray-level images. The results indicate that a model that combines a bottom-up feature aggregation strategy with recurrent processing is best suited for describing this type of perceptual completion. >
Anisotropy of the electron-phonon collision frequency on the Fermi surface of silver

The anisotropy of the electron-phonon collision frequency on the Fermi surface of silver has been determined from the temperature dependence of radio-frequency-size-effect (RFSE) line amplitudes. In all cases when ln $A$ is plotted vs ${T}^{3}$ the expected linear relationship is observed. The average collision frequency for 18 parallel field orbits on the Fermi surface was determined from the temperature dependence of the corresponding RFSE signals. Using these average values in an inversion technique, a functional form for the collision frequency over the Fermi surface was determined. Several different functional forms were assumed in order to determine the best fit to the experimental data. In addition the average collision frequency about five tilted field orbits has been determined. These average values are in reasonable agreement with values calculated from the derived analytic expression of the collision frequency.
PYROLYSIS AND ISOMERIZATION OF QUADRICYCLANE, NORBORNADIENE, AND TOLUENE

Pyrolysis and isomerization of the C7H8 isomers quadricyclane, norbornadiene, cycloheptatriene, and toluene have been studied in a micro-flow tube reactor with detection/isomer identification by guided-ion beam tandem mass spectrometry. The methodology permits pyrolysis studies with product isomer identification with samples of only a few milligrams. The samples are premixed with buffer gas and then passed through a temperature-controlled flow tube reactor with millisecond residence time. The sample/pyrolysis products exiting the flow tube are ionized by methane chemical ionization (CI), and both CI mass spectra and low-energy collision-induced dissociation were used to determine the composition. For the experimental residence time, toluene and cycloheptatriene are stable over the 298−998 K temperature range. Decomposition of norbornadiene sets in at ∼600 K. The dominant decomposition processes are acetylene elimination via a retro-Diels−Alder reaction and isomerization to toluene. Quadricyclane begins to...
Some implications of relative biomechanical neck length in hominid femora

Demonstration of a size-based influence on relative biomechanical neck length of the femur predicts relatively longer necks for smaller femurs. Fossil hominids through the middle Pleistocene appear to have relatively longer femur necks than expected from this relation, excepting the two small australopithecine females. It is suggested that this variation results from smaller crania at birth in the fossils, and the possibility is raised that australopithecine populations were characterized by marked brain size differences at birth.
Coping with the Disruptive College Student: A Practical Model

This article offers some practical suggestions for college faculty and administrators who must deal with disruptive students. It emphasizes the need for well-defined codes of student conduct and clear procedures for dealing with disruptive behavior, and considers why university officials may be reluctant to deal with disciplinary problems related to psychiatric disturbances. The author discusses the distinction between disruptive and dangerous behavior, looks at the use of mandatory psychiatric withdrawals, makes recommendations for conditions of re-enrollment, and suggests how a campus mental health service can be useful in dealing with the problem.
Molecular cloning of p67, a lysosomal membrane glycoprotein from Trypanosoma brucei

We have previously characterized a highly glycosylated membrane protein (p67) in Trypanosoma brucei spp that is apparently targeted to lysosomes in a developmentally regulated manner. Antibody to native p67 identified a partial cDNA clone from a T. b. rhodesiense expression library and RT-PCR was used to complete the sequence of the cDNA. Equal levels of p67 transcript are detected in both procyclic and bloodstream stages of the life cycle. The 2771 nt cDNA contains a 1980 nt orf encoding a 659 amino acid polypeptide (72,567 Da). Hydropathy analysis predicts a Type I membrane topology (N to C): an N-terminal signal sequence, a large hydrophilic lumenal domain with 14 N-glycosylation sites, a trans-membrane domain (19 aa), and a short (24 aa) cytoplasmic domain. Peptide microsequencing of purified p67 identified nine residues identical to the deduced amino acid sequence, confirming the identity of the cDNA and defining the signal sequence cleavage site. Antibody to p67 protein produced in E. coli recognizes the same spectrum of native p67 glycoforms as the antibody used to clone the cDNA. All features of the deduced amino acid sequence are consistent with the known properties of the native protein and suggest a structure similar to mammalian LAMPS. The cytoplasmic domain contains two putative di-leucine targeting motifs similar to those involved in lysosomal targeting in vertebrate cells. Our results suggest that a single p67 polypeptide, or a group of highly related polypeptides, is synthesized in both bloodstream and procyclic trypanosomes and that subsequent post-translational processing and lysosomal targeting is subject to stage-specific regulation.
Rhetorical vision of equality: Analysis of the rhetoric of the southern black press during reconstruction

The present study examines the rhetorical vision of equality by southern black journalists during Reconstruction. Their vision illuminates key areas of needs, themes, heroes or villains which are shared by most blacks who believed equal rights essential for full participation in a society reluctant to share them. The black press, then, puts forth a vision significantly different from ones developed in most southern papers, and advances one more atuned with the changing views of blacks during Reconstruction.