cssi/SciSciGPT-SciSciNet huggingface dataset:
Exon-Mediated Activation of Transcription Starts.

Summary The processing of RNA transcripts from mammalian genes occurs in proximity to their transcription. Here, we describe a phenomenon affecting thousands of genes that we call exon-mediated activation of transcription starts (EMATS), in which the splicing of internal exons impacts promoter choice and the expression level of the gene. We observed that evolutionary gain of internal exons is associated with gain of new transcription start sites (TSSs) nearby and increased gene expression. Inhibiting exon splicing reduced transcription from nearby promoters, and creation of new spliced exons activated transcription from cryptic promoters. The strongest effects occurred for weak promoters located proximal and upstream of efficiently spliced exons. Together, our findings support a model in which splicing recruits transcription machinery locally to influence TSS choice and identify exon gain, loss, and regulatory change as major contributors to the evolution of alternative promoters and gene expression in mammals.
Adversarial Example Detection and Classification with Asymmetrical Adversarial Training

The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the attacker is oblivious to the detection mechanism. In this paper, we consider the adversarial detection problem under the robust optimization framework. We partition the input space into subspaces and train adversarial robust subspace detectors using asymmetrical adversarial training (AAT). The integration of the classifier and detectors presents a detection mechanism that provides a performance guarantee to the adversary it considered. We demonstrate that AAT promotes the learning of class-conditional distributions, which further gives rise to generative detection/classification approaches that are both robust and more interpretable. We provide comprehensive evaluations of the above methods, and demonstrate their competitive performances and compelling properties on adversarial detection and robust classification problems.
Three-dimensional near-field analysis through peak force scattering-type near-field optical microscopy.

Scattering-type scanning near-field optical microscopy (s-SNOM) is instrumental in exploring polaritonic behaviors of two-dimensional (2D) materials at the nanoscale. A sharp s-SNOM tip couples momenta into 2D materials through phase matching to excite phonon polaritons, which manifest as nanoscale interference fringes in raster images. However, s-SNOM lacks the ability to detect the progression of near-field properties along the perpendicular axis to the surface. Here, we perform near-field analysis of a micro-disk and a reflective edge made of isotopically pure hexagonal boron nitride (h-11BN), by using three-dimensional near-field response cubes obtained by peak force scattering-type near-field optical microscopy (PF-SNOM). Momentum quantization of polaritons from the confinement of the circular structure is revealed in situ. Moreover, tip–sample distance is found to be capable of fine-tuning the momentum of polaritons and modifying the superposition of quantized polaritonic modes. The PF-SNOM-based three-dimensional near-field analysis provides detailed characterization capability with a high spatial resolution to fully map three-dimensional near-fields of nano-photonics and polaritonic structures.
Conserved Quantities in General Relativity: From the Quasi-Local Level to Spatial Infinity

We define quasi-local conserved quantities in general relativity by using the optimal isometric embedding in Wang and Yau (Commun Math Phys 288(3):919–942, 2009) to transplant Killing fields in the Minkowski spacetime back to the 2-surface of interest in a physical spacetime. To each optimal isometric embedding, a dual element of the Lie algebra of the Lorentz group is assigned. Quasi-local angular momentum and quasi-local center of mass correspond to pairing this element with rotation Killing fields and boost Killing fields, respectively. They obey classical transformation laws under the action of the Poincare group. We further justify these definitions by considering their limits as the total angular momentum and the total center of mass of an isolated system. These expressions were derived from the Hamilton–Jacobi analysis of the gravitational action and thus satisfy conservation laws. As a result, we obtained an invariant total angular momentum theorem in the Kerr spacetime. For a vacuum asymptotically flat initial data set of order 1, it is shown that the limits are always finite without any extra assumptions. We also study these total conserved quantities on a family of asymptotically flat initial data sets evolving by the vacuum Einstein evolution equation. It is shown that the total angular momentum is conserved under the evolution. For the total center of mass, the classical dynamical formula relating the center of mass, energy, and linear momentum is recovered, in the nonlinear context of initial data sets evolving by the vacuum Einstein evolution equation. The definition of quasi-local angular momentum provides an answer to the second problem in classical general relativity on Penrose’s list (Proc R Soc Lond Ser A 381(1780):53–63, 1982).
Electrostatics, Hydrogen Bonding, and Molecular Structure at Polycation and Peptide:Lipid Membrane Interfaces

Polycation and peptide-modified surfaces represent opportunities for developing potentially novel biocidal materials in a growing effort to combat bacterial resistance to traditional bactericides. ...
Extremal metrics on blowups along submanifolds

We give conditions under which the blowup of an extremal Kahler manifold along a submanifold of codimension greater than two admits an extremal metric. This generalizes work of Arezzo–Pacard–Singer, who considered blowups in points.
Boron arsenide heterostructures: lattice-matched heterointerfaces and strain effects on band alignments and mobility

BAs is a III–V semiconductor with ultra-high thermal conductivity, but many of its electronic properties are unknown. This work applies predictive atomistic calculations to investigate the properties of BAs heterostructures, such as strain effects on band alignments and carrier mobility, considering BAs as both a thin film and a substrate for lattice-matched materials. The results show that isotropic biaxial in-plane strain decreases the band gap independent of sign or direction. In addition, 1% biaxial tensile strain increases the in-plane electron and hole mobilities at 300 K by >60% compared to the unstrained values due to a reduction of the electron effective mass and of hole interband scattering. Moreover, BAs is shown to be nearly lattice-matched with InGaN and ZnSnN2, two important optoelectronic semiconductors with tunable band gaps by alloying and cation disorder, respectively. The results predict type-II band alignments and determine the absolute band offsets of these two materials with BAs. The combination of the ultra-high thermal conductivity and intrinsic p-type character of BAs, with its high electron and hole mobilities that can be further increased by tensile strain, as well as the lattice-match and the type-II band alignment with intrinsically n-type InGaN and ZnSnN2 demonstrate the potential of BAs heterostructures for electronic and optoelectronic devices.
Moduli spaces of witch curves topologically realize the $2$-associahedra

For $r \geq 1$ and $\mathbf{n} \in \mathbb{Z}_{\geq0}^r\setminus\{\mathbf{0}\}$, we construct the compactified moduli space $\overline{2\mathcal{M}}_{\mathbf{n}}$ of witch curves of type $\mathbf{n}$. We equip $\overline{2\mathcal{M}}_{\mathbf{n}}$ with a stratification by the 2-associahedron $W_{\mathbf{n}}$, and prove that $\overline{2\mathcal{M}}_{\mathbf{n}}$ is compact and metrizable. In addition, we show that the forgetful map $\overline{2\mathcal{M}}_{\mathbf{n}} \to \overline{\mathcal{M}}_r$ to the moduli space of stable disk trees is continuous and respects the stratifications.
High-precision local transfer of van der Waals materials on nanophotonic structures

Prototyping of van der Waals materials on dense nanophotonic devices requires high-precision monolayer discrimination to avoid bulk material contamination. We use the glass transition temperature of polycarbonate, used in the standard dry transfer process, to draw an in situ point for the precise pickup of two-dimensional materials. We transfer transition metal dichalcogenide monolayers onto a large-area silicon nitride spiral waveguide and silicon nitride ring resonators to demonstrate the high-precision contamination-free nature of the modified dry transfer method. Our improved local transfer technique is a necessary step for the deterministic integration of high-quality van der Waals materials onto nanocavities for the exploration of few-photon nonlinear optics on a high-throughput, nanofabrication-compatible platform.
Dynamics of a Ring of Diffusively Coupled Lorenz Oscillators

We study the dynamics of a finite chain of diffusively coupled Lorenz oscillators with periodic boundary conditions. Such rings possess infinitely many fixed states, some of which are observed to be stable. It is shown that there exists a stable fixed state in arbitrarily large rings for a fixed coupling strength. This suggests that coherent behavior in networks of diffusively coupled systems may appear at a coupling strength that is independent of the size of the network.
The Continuous Wavelet Transform and Symmetric Spaces

The continuous wavelet transform has become a widely used tool in applied science during the last decade. In this article we discuss some generalizations coming from actions of closed subgroups of GL(n, R) acting on R n . In particular, we propose a way to invert the wavelet transform in the case where the stabilizer of a generic point in R n is not compact, but a symmetric subgroup, a case that has not previously been discussed in the literature. Introduction, Wavelets on ax +b-group The continuous wavelet transform has become a widely used tool in applied science during the last decade. The best known example deals with wavelets on the real line. Each wavelet by taking "matrix coefficients", i.e., inner products with translations and dilations of the wavelet, defines a wavelet transform which can be used to reconstruct the function from the dilations and translations of the wavelet. This process helps compensate for the local-nonlocal behavior of the Fourier transform. Translations and dilations form the so-called ax + b-group of transformations of the real line. These act in a natural manner as unitary operators on L 2 (R). In that way, wavelet transforms are simply a part of the representation theory of the ax + b-group. This observation is the basis for the generalization of the continuous wavelet transform to higher dimensions and more general settings. There have been further attempts to generalize these ideas to arbitrary groups; see (4) and the references therein. In this article we review some of the basic ideas for the general wavelet transform for groups acting on R n . We start by reviewing the classical one dimensional wavelet transform. In this simple setting, the usual definition of a wavelet is equivalent to the corresponding matrix coefficients being square integrable on theax+b-group. A natural generalization of this to an arbitrary representation (�, H) of a topological group G is to say, that a vector u ∈ H \ {0} is a wavelet if a 7→ (v | �(a)u) is square integrable for all v ∈ H. The generalized wavelet transform is then Wv(a) := (v | �(a)v). The basic facts for this transform and, in particular, the inversion formula are presented in section 1. In section 2 these notions are applied to topological groups H acting on R n by a representation �. Let G = H ×� R n be the semi-direct product of H and R n . Then G acts on R n and L 2 (R n ). When the action of H on R n is sufficiently well behaved, the decomposition of L 2 (R n ) under G can be described in terms of the orbits in R n under the transpose (contragredient) representation � ' where � ' (a) = �(a −1 ) t . In general, one
Internal-transfer weighting of multi-task learning for lung cancer detection

Deep learning has achieved many successes in medical imaging, including lung nodule segmentation and lung cancer prediction on computed tomography (CT). Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main/primary" task. However, balancing the optimization criteria of multi-task networks across different tasks is an area of active exploration. Here, we extend a previously proposed 3D attention-based network with four additional multi-task subnetworks for the detection of lung cancer and four auxiliary tasks (diagnosis of asthma, chronic bronchitis, chronic obstructive pulmonary disease, and emphysema). We introduce and evaluate a learning policy, Periodic Focusing Learning Policy (PFLP), that alternates the dominance of tasks throughout the training. To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training. To evaluate this approach, we examined 3386 patients (single scan per patient) from the National Lung Screening Trial (NLST) and de-identified data from the Vanderbilt Lung Screening Program, with a 2517/277/592 (scans) split for training, validation, and testing. Baseline networks include a single-task strategy and a multi-task strategy without adaptive weights (PFLP/ITW), while primary experiments are multi-task trials with either PFLP or ITW or both. On the test set for lung cancer prediction, the baseline single-task network achieved prediction AUC of 0.8080 and multi-task baseline failed to converge (AUC 0.6720). However, applying PFLP helped multi-task network clarify and achieved test set lung cancer prediction AUC of 0.8402. Furthermore, our ITW technique boosted the PFLP enabled multi-task network and achieved an AUC of 0.8462 (McNemar test, p < 0.01). In conclusion, adaptive consideration of multi-task learning weights is important, and PFLP and ITW are promising strategies.
Spectral asymptotics on sequences of elliptically degenerating Riemann surfaces

This is the second in a series of two articles where we study various aspects of the spectral theory associated to families of hyperbolic Riemann surfaces obtained through elliptic degeneration. In the first article, we investigate the asymptotics of the trace of the heat kernel both near zero and infinity and we show the convergence of small eigenvalues and corresponding eigenfunctions. Having obtained necessary bounds for the trace, this second article presents the behavior of several spectral invariants. Some of these invariants, such as the Selberg zeta function and the spectral counting functions associated to small eigenvalues below 1/4, converge to their respective counterparts on the limiting surface. Other spectral invariants, such as the spectral zeta function and the logarithm of the determinant of the Laplacian diverge. In these latter cases, we identify diverging terms and remove their contributions, thus regularizing convergence of these spectral invariants. Our study is motivated by a result from \cite{He 83}, which D. Hejhal attributes to A. Selberg, proving spectral accumulation for the family of Hecke triangle groups. In this article, we obtain a quantitative result to Selberg's remark.
Topoisomerases Modulate the Timing of Meiotic DNA Breakage and Chromosome Morphogenesis in Saccharomyces cerevisiae.

Meiotic chromosomes experience substantial strain due to morphological changes and ongoing metabolism of the DNA. Outside of meiosis, topology-modifying enzymes called topoisomerases manage this sort of strain, but their function during meiotic prophase has not been thoroughly explored... During meiotic prophase, concurrent transcription, recombination, and chromosome synapsis place substantial topological strain on chromosomal DNA, but the role of topoisomerases in this context remains poorly defined. Here, we analyzed the roles of topoisomerases I and II (Top1 and Top2) during meiotic prophase in Saccharomyces cerevisiae. We show that both topoisomerases accumulate primarily in promoter-containing intergenic regions of actively transcribing genes, including many meiotic double-strand break (DSB) hotspots. Despite the comparable binding patterns, top1 and top2 mutations have different effects on meiotic recombination. TOP1 disruption delays DSB induction and shortens the window of DSB accumulation by an unknown mechanism. By contrast, temperature-sensitive top2-1 mutants exhibit a marked delay in meiotic chromosome remodeling and elevated DSB signals on synapsed chromosomes. The problems in chromosome remodeling were linked to altered Top2 binding patterns rather than a loss of Top2 catalytic activity, and stemmed from a defect in recruiting the chromosome remodeler Pch2/TRIP13 to synapsed chromosomes. No chromosomal defects were observed in the absence of TOP1. Our results imply independent roles for Top1 and Top2 in modulating meiotic chromosome structure and recombination.
Improving Transferability of Deep Neural Networks

Learning from small amounts of labeled data is a challenge in the area of deep learning. This is currently addressed by Transfer Learning, where one learns the small dataset as a transfer task from a larger source dataset. Transfer Learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate for the layers of the neural network. We show through experiments on the ImageNet22k and Oxford Flowers datasets that improvements in accuracy in range of 127% can be obtained by proper choice of learning rates. We also show that the images/label parameter for a dataset can potentially be used to determine optimal learning rates for the layers to get the best overall accuracy. We additionally validate this method on a sample of real-world image classification tasks from a public visual recognition API.
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability

We consider the general (stochastic) contextual bandit problem under the realizability assumption, i.e., the expected reward, as a function of contexts and actions, belongs to a general function class $\mathcal{F}$. We design a fast and simple algorithm that achieves the statistically optimal regret with only ${O}(\log T)$ calls to an offline least-squares regression oracle across all $T$ rounds. The number of oracle calls can be further reduced to $O(\log\log T)$ if $T$ is known in advance. Our results provide the first universal and optimal reduction from contextual bandits to offline regression, solving an important open problem in contextual bandits. A direct consequence of our results is that any advances in offline regression immediately translate to contextual bandits, statistically and computationally. This leads to faster algorithms and improved regret guarantees for broader classes of contextual bandit problems.
Control of thermal deflection in concrete structures using iron-based shape memory alloys

Mitigating the structural damage caused by thermal expansion cycles is a primary objective in the design of concrete structures, such as bridges or buildings. One method to achieve this goal is the introduction of shape memory alloys (SMAs) as a replacement of traditional steel reinforcements in concrete structures. SMAs exhibit a characteristic known as “shape memory effect,” which allows the recovery of large deformations through the alloy’s martensite and austenite phase transformations. This effect gives SMAs an inherent advantage over steel. The purpose of this paper is to characterize the effect of an embedded SMA rod on a concrete system undergoing a thermal cycle, and to optimize the configuration of these materials. To achieve these ends, a system is modeled in Abaqus, a software suite for finite element analysis, consisting of a concrete block with an embedded, prestrained SMA rod, in which the concrete and SMA material properties have been determined from experimentation and secondary research. A set of the SMA’s properties (max transformation strain, coefficient of thermal expansion, stress influence coefficients, and volume fraction of SMA to concrete) are iteratively altered to produce characterization of the rod’s effect on the system, and then the same set are again altered using a multi-objective optimization tool to minimize deflection and maximize the temperature where concrete damage occurs. This approach is a cost-effective method to characterize the effects of these material properties and produce results that can be utilized in future projects where SMAs are deployed in large-scale concrete structures.
Effect of Organic Alkalinity on Seawater Buffer Capacity: A Numerical Exploration

Organic alkalinity is a poorly understood component of total titration alkalinity in aquatic environments. Using a numerical method, the effects of organic acid (HOA) and its conjugate base (OA−) on seawater carbonate chemistry and buffer behaviors, as well as those in a hypothetical estuarine mixing zone, are explored under both closed- and open-system conditions. The simulation results show that HOA addition leads to pCO2 increase and pH decrease in a closed system when total dissolved inorganic carbon (DIC) remains the same. If opened to the atmosphere (pCO2 = 400 µatm), CO2 degassing and re-equilibration would cause depressed pH compared to the unperturbed seawater, but the seawater buffer to pH change $$\left( {\beta _{{{\text{DIC}}}} \, = \left( {\frac{{\partial \ln \left( {\left[ {{\text{H}}^{ + } } \right]} \right)}}{{\partial {\text{DIC}}}}} \right)^{{ - 1}} } \right)$$ indicates that weaker organic acid (i.e., higher pKa) results in higher buffer capacity (greater βDIC) than the unperturbed seawater. In comparison, OA− (with accompanying cations) in the form of net alkalinity addition leads to pCO2 decrease in a closed system. After re-equilibrating with the atmosphere, the resulting perturbed seawater has higher pH and βDIC than the unperturbed seawater. If river water has organic alkalinity, pH in the estuarine mixing zone is always lower than those caused by a mixing between organic alkalinity-free river (at constant total alkalinity) and ocean waters, regardless of the pKa values. On the other hand, organic alkalinity with higher pKa provides slightly greater βDIC in the mixing zone, and that with lower pKa either results in large CO2 oversaturation (closed system) or reduced βDIC (in mid to high salinity in the closed system or the entire mixing zone in the open system). Finally, despite the various effects on seawater buffer through either HOA or OA− addition, destruction of organic molecules including organic alkalinity via biogeochemical reactions should result in a net CO2 loss from seawater. Nevertheless, the significance of this organic alkalinity, especially that comes from organic acids that are not accounted for under the currently recognized “zero proton level” (Dickson in Deep Sea Res 28:609–623, 1981), remains unknown thus a potentially interesting and relevant research topic in studying oceanic alkalinity cycle.
Post-Durban Climate Policy Architecture Based on Linkage of Cap-and-Trade Systems

AbstractThe outcome of the December 2011 United Nations climate negotiations in Durban, South Africa, provides an important new opportunity to move toward an international climate policy architecture that is capable of delivering broad international participation and significant global CO2 emissions reductions at reasonable cost. We evaluate one important component of potential climate polig architecture for the post-Durban era: links among independent tradable permit systems for greenhouse gases, because linkage reduces the cost of achieving given targets, there is tremendous pressure to link existing and planned cap-and-trade systems, and in fact, a number of links already or will soon exist. We draw on recent political and economic experience with linkage to evaluate potential roles that linkage may play in post-Durban international climate policy, both in a near-term, de facto architecture of indirect links between regional, national, and sub-national cap-and-trade systems, and in a longer-term, more comprehensive bottom-up architecture of direct links. Although linkage will certainly help to reduce long-term abatement costs, it may also serve as an effective mechanism for building institutional and political structure to support a future climate agreement.Table of ContentsI. Introduction 405II. Theory of Linkage 407A. Types of Linkage 4071. Direct linkage 4072. Indirect linkages 408B. Implications of Linking 4081. Cost effectiveness 4092. Distribution of policy impacts 4093. National control 4104. Incentives for setting caps 410III. Characteristics of Existing and Proposed Linkages 411A. Linkage Among Existing Trading Systems 4111. Emissions trading under Article 17 of the Kyoto Protocol 4112. Clean Development Mechanism 4123. Joint Implementation 4144. European Union Emissions Trading System 4155. Norwegian Emissions Trading Scheme 4176. Swiss C02 Act 4187. New Zealand Emissions Trading Scheme 4188. Regional Greenhouse Gas Initiative 420B. Possible Linkage Among Future Trading Systems 4221. Australia's Clean Energy Act of 2011 4222. California's Global Warming Solutions Act of 2006 4233. Quebec's cap-and-trade system 4244. China's emissions trading system pilots 4245. South Korea's cap-and-trade system 4256. Mexico's cap-and-trade system 4257. American Clean Energy and Security Act of 2009 425IV. The Durban Platform and Its Implications for Linkage 426A. The UNFCCC, the Berlin Mandate, and the Kyoto Protocol 426B. A New Approach Requiring Participation of All Parties 427V. Linkage as a Climate Policy Architecture 428A. Near-Term Role …
Chapter 2 – ‘Stemness’: Definitions, Criteria, and Standards

Stem cells have generated more public and professional interest than almost any other topic in biology. One reason stem cells capture the imagination of so many is the hope that understanding their unique properties may reveal paths leading to new treatments for a variety of degenerative illnesses. And although the field of stem cell biology has grown rapidly, considerable confusion and disagreement as to the nature of stem cells exists. This confusion can be partly attributed to the sometimes idiosyncratic terms and definitions used to describe stem cells. Although definitions can be restrictive, they are useful when they provide a foundation for mutual understanding and experimental standardization. This chapter presents some definitions of stem cells, discusses the origin of stem cells, and suggests criteria or standards for identifying, isolating, and characterizing stem cells. Finally, the notion of ‘stemness’ and its possible application in understanding stem cell biology is explained.
Analytical study on phase transition of shape memory alloy wire under uniaxial tension

Abstract This paper considers the stress-induced phase transitions of shape memory alloy slender cylinder, and analytically studies the phase transition process and the associated instability. A three-dimensional (3D) phenomenological model with an internal variable is adopted, which is simplified to a 1D system of two strains in three regions (austenite, martensite and phase transition regions). Suitable boundary conditions and interface conditions are proposed. Theoretically, it is a free boundary problem, as the position and shape of phase interfaces are unknown. We then focus on planar interfaces (which are energetically favored), and a symmetric case when phase transition occurs in the middle. For given applied stress, two-region solutions, three-region solutions and the connecting solutions between them are obtained analytically or semi-analytically, including many period-k solutions. Two-region solutions show that phase transition does not take place at one point, but simultaneously in a small region. The width of phase transition region is found analytically, revealing the roles of the material and geometrical parameters. Three-region solutions represent localized inhomogeneous deformations in experiments, and capture that the stress stays almost at the Maxwell stress during propagation of transformation front. For displacement-controlled process, the transition process is demonstrated by the stress-strain curve, which captures the stress drop/rise and the transition from homogeneous deformations to period-1 localized inhomogeneous deformations. When the radius is smaller than a critical value (given by material constants), the stress drop is very sharp due to transition of solutions in a snap-back bifurcation. These features show good agreement with experimental observations and shed light on the difficulties of numerical simulations.
Early shaping of asymmetrical planetary nebulae

It is suggested that the shape of a young asymmetrical planetary nebulae may be influenced by a close binary-star located at its center. This binary is a relic of the common envelope phase, presumably through which the asymmetrical planetary nebula evolved. It is assumed that, for a short period of time, shortly after the cessation of the slow wind and long before the fast wind becomes effective, the binary ejects a small amount of mass, mainly in the equatorial plane. By using two-dimensional hydrodynamics it is found that, at late times, the high-density region has a 'horsehoe' shape, as viewed in the symmetry plane. There is an instability in the maximum density region, which shows up as a high-density knot. The exact scale of the knot is determined by the numerics. The applications of the results to observations are discussed.
The motion of a satellite in resonance with the longitude-dependent harmonics

The solution to the motion of a satellite in an eccentric orbit and in resonance with one or more of the longitude-dependent harmonics of the central planet is developed. The method of solution parallels the well known von Zeipel method of general perturbations. The solution consists of expressions for the variations of the Delaunay variables. These expressions are composed of the perturbations developed by Brouwer in 1959 for the motion of an artificial satellite plus first-order resonant perturbations due to longitude-dependent harmonics (in terms of Legendre normal elliptic integrals of the first and second kind).
Human-computer interaction

Human?computer interaction (HCI) is the area of intersection between psychology and the social sciences, on the one hand, and computer science and technology, on the other. HCI researchers analyse and design-specific user-interface technologies (e.g. three-dimensional pointing devices, interactive video). They study and improve the processes of technology development (e.g. usability evaluation, design rationale). They develop and evaluate new applications of technology (e.g. computer conferencing, software design environments). Through the past two decades, HCI has progressively integrated its scientific concerns with the engineering goal of improving theusabilityof computer systems and applications, thus establishing a body of technical knowledge and methodology. HCI continues to provide a challenging test domain for applying and developing psychology and social science in the context of technology development and use.
Prior-Independent Optimal Auctions

Auctions are widely used in practice. Although auctions are also extensively studied in the literature, most of the developments rely on the significant common prior assumption. We study the design...
Orbit Determination at a Single Ground Station Using Range Rate Data

The solution of the spacecraft orbit determination problem is highly dependent upon the type of tracking data available at the ground station. Many algorithms exist for different types and combinations of tracking data. Well-funded tracking stations are typically able to supply six or more observed parameters simultaneously, enabling explicit solution of the orbit determination problem. The low-cost ground station under development at San Jose State University will initially be able to measure only Doppler shift as a function of time, thereby providing slant range rate as the single observable parameter. An algorithm enabling explicit solution of the orbit determination problem using range rate exclusively was not found, but orbit estimate improvement is still possible via differential correction. By utilizing a differential correction scheme in conjunction with a batch least squares estimator along with convergence improvements, the task of maintaining an acceptably accurate knowledge of the spacecraft orbit is made possible for a single ground station receiving a single measured parameter, even at higher levels of measurement noise and bias than those expected at the ground station.
Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders.

Abstract Background Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. Methods We worked with T1 structural magnetic resonance imaging data collected from 1493 individuals comprising healthy control subjects, patients with psychosis, and their unaffected first-degree relatives. Specifically, the dataset included 176 bipolar disorder probands, 134 schizoaffective disorder probands, 240 schizophrenia probands, 362 control subjects, and 581 patient relatives. We assumed that there might be noise in the diagnostic labeling process. We detected label noise by classifying the data multiple times using a support vector machine classifier, and then we flagged those individuals in which all classifiers unanimously mislabeled those subjects. Next, we assigned a new diagnostic label to these individuals, based on the biological data (magnetic resonance imaging), using an iterative data cleansing approach. Results Simulation results showed that our method was highly accurate in identifying label noise. Both diagnostic and biotype categories showed about 65% and 63% of noisy labels, respectively, with the largest amount of relabeling occurring between the healthy control subjects and individuals with bipolar disorder and schizophrenia as well as in unaffected close relatives. The extraction of imaging features highlighted regional brain changes associated with each group. Conclusions This approach represents an initial step toward developing strategies that need not assume that existing mental health diagnostic categories are always valid but rather allows us to leverage this information while also acknowledging that there are misassignments.
Koopman spectra in reproducing kernel Hilbert spaces

Abstract Every invertible, measure-preserving dynamical system induces a Koopman operator, which is a linear, unitary evolution operator acting on the L 2 space of observables associated with the invariant measure. Koopman eigenfunctions represent the quasiperiodic, or non-mixing, component of the dynamics. The extraction of these eigenfunctions and their associated eigenfrequencies from a given time series is a non-trivial problem when the underlying system has a dense point spectrum, or a continuous spectrum behaving similarly to noise. This paper describes methods for identifying Koopman eigenfrequencies and eigenfunctions from a discretely sampled time series generated by such a system with unknown dynamics. Our main result gives necessary and sufficient conditions for a Fourier function, defined on N states sampled along an orbit of the dynamics, to be extensible to a Koopman eigenfunction on the whole state space, lying in a reproducing kernel Hilbert space (RKHS). In particular, we show that such an extension exists if and only if the RKHS norm of the Fourier function does not diverge as N → ∞ . In that case, the corresponding Fourier frequency is also a Koopman eigenfrequency, modulo a unique translate by a Nyquist frequency interval, and the RKHS extensions of the N-sample Fourier functions converge to a Koopman eigenfunction in RKHS norm. For Koopman eigenfunctions in L 2 that do not have RKHS representatives, the RKHS extensions of Fourier functions at the corresponding eigenfrequencies are shown to converge in L 2 norm. Numerical experiments on mixed-spectrum systems with weak periodic components demonstrate that this approach has significantly higher skill in identifying Koopman eigenfrequencies compared to conventional spectral estimation techniques based on the discrete Fourier transform.
Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. Each synapse and neuron behave as filters capable of preserving temporal information. As such neuron dynamics and filter effects are ignored in existing training algorithms, the SNN downgrades into a memoryless system and loses the ability of temporal signal processing. Furthermore, spike timing plays an important role in information representation, but conventional rate-based spike coding models only consider spike trains statistically, and discard information carried by its temporal structures. To address the above issues, and exploit the temporal dynamics of SNNs, we formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights. The proposed model and training algorithm are applied to construct associative memories and classifiers for synthetic and public datasets including MNIST, NMNIST, DVS 128 etc.; and their accuracy outperforms state-of-art approaches.
Robustness of SAR Refraction Autofocus to Power-Law Errors

A recent analysis has yielded a non-parametric methodology which automatically estimates and compensates for atmospheric refraction defocus in synthetic aperture radar (SAR) imagery. Such deleterious defocus effects result from the delay and bending of the individual SAR radar pulses as they refraction through the atmosphere. A primary benefit of the recent data-driven refraction autofocus is that it is robust to non-parametric delay and bending errors which can vary from one pulse to the next, as arises due to changing atmospheric conditions along the synthetic aperture of the radar's trajectory. The current investigation examines the robustness of these techniques for realistic scenarios in which the refraction-induced delay and bending vary in time according to a power-law spectrum. Specifically, this refraction autofocus methodology yields sharp scene focus when applied to measured Ku-band SAR imagery in which power-law errors have been injected into the delay and bending of the radar pulses.
Will it leak? Will it burst? COPV perforation and rupture after a MMOD impact

Abstract Most spacecraft have at least one pressurized vessel on board. Because of the serious damage that might result following a high-speed on-orbit space debris particle impact, a primary design consideration is the anticipation and mitigation of that damage. Depending on pressure vessel design and impact / operating conditions, a pressure vessel impacted at hypervelocity may experience either only relatively shallow damage; a through-hole, perhaps with localized liner cracking or composite peeling; or catastrophic failure (rupture). While a puncture and the resulting leak could de-stabilize an orbiting spacecraft, an on-orbit rupture could not only lead to spacecraft loss, but for human missions, possibly loss of life. Herein we present the development of a ballistic limit equation (BLE) and a rupture limit equation (RLE) for composite overwrapped pressure vessels. Similar to a BLE that can be used to characterize whether or not a pressure vessel would be punctured by a high-speed impact, an RLE is designed to differentiate between regions of operating and impact conditions that, given a perforation, would result in either a rupture or would result only in a relatively small hole or crack. In a risk assessment that considers the various failures that might occur following debris impact, both types of equations are required. Comparisons of the RLE and the BLE developed herein with experimental results shows that both equations are able to cleanly separate the regions of rupture from non-rupture, and perforation from non-perforation. As such, the equations presented are both highly accurate in predicting the response of the COPVs and impact conditions considered.
Learning in Gated Neural Networks.

Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural networks. The backbone of such gated networks is a mixture-of-experts layer, where several experts make regression decisions and gating controls how to weigh the decisions in an input-dependent manner. Despite having such a prominent role in both modern and classical machine learning, very little is understood about parameter recovery of mixture-of-experts since gradient descent and EM algorithms are known to be stuck in local optima in such models. In this paper, we perform a careful analysis of the optimization landscape and show that with appropriately designed loss functions, gradient descent can indeed learn the parameters accurately. A key idea underpinning our results is the design of two {\em distinct} loss functions, one for recovering the expert parameters and another for recovering the gating parameters. We demonstrate the first sample complexity results for parameter recovery in this model for any algorithm and demonstrate significant performance gains over standard loss functions in numerical experiments.
A spectral bound on hypergraph discrepancy

Let $\mathcal{H}$ be a $t$-regular hypergraph on $n$ vertices and $m$ edges. Let $M$ be the $m \times n$ incidence matrix of $\mathcal{H}$ and let us denote $\lambda =\max_{v \perp \overline{1},\|v\| = 1}\|Mv\|$. We show that the discrepancy of $\mathcal{H}$ is $O(\sqrt{t} + \lambda)$. As a corollary, this gives us that for every $t$, the discrepancy of a random $t$-regular hypergraph with $n$ vertices and $m \geq n$ edges is almost surely $O(\sqrt{t})$ as $n$ grows. The proof also gives a polynomial time algorithm that takes a hypergraph as input and outputs a coloring with the above guarantee.
Carbohydrate-binding domains facilitate efficient oligosaccharides synthesis by enhancing mutant catalytic domain transglycosylation activity

Chemoenzymatic approaches using carbohydrate-active enzymes (CAZymes) offer a promising avenue for the synthesis of glycans like oligosaccharides. Here, we report a novel chemoenzymatic route for cellodextrins synthesis employed by chimeric CAZymes, akin to native glycosyltransferases, involving the unprecedented participation of a "non-catalytic" lectin-like domain or carbohydrate-binding modules (CBMs) in the catalytic step for glycosidic bond synthesis using β-cellobiosyl donor sugars as activated substrates. CBMs are often thought to play a passive substrate targeting role in enzymatic glycosylation reactions mostly via overcoming substrate diffusion limitations for tethered catalytic domains (CDs) but are not known to participate directly in any nucleophilic substitution mechanisms that impact the actual glycosyl transfer step. This study provides evidence for the direct participation of CBMs in the catalytic reaction step for β-glucan glycosidic bonds synthesis enhancing activity for CBM-based CAZyme chimeras by >140-fold over CDs alone. Dynamic intradomain interactions that facilitate this poorly understood reaction mechanism were further revealed by small-angle X-ray scattering structural analysis along with detailed mutagenesis studies to shed light on our current limited understanding of similar transglycosylation-type reaction mechanisms. In summary, our study provides a novel strategy for engineering similar CBM-based CAZyme chimeras for the synthesis of bespoke oligosaccharides using simple activated sugar monomers.
Probabilistic Ranking Of Microbiomes Plus Taxa Selection to discover and validate microbiome function models for multiple litter decomposition studies

The overwhelming complexity of microbiomes makes it difficult to decipher functional relationships between specific microbes and ecosystem properties. While machine learning analyses have demonstrated an impressive ability to correlate microbial community composition with macroscopic functions, mechanisms that dictate model predictions are often unknown, and predictions often lack an assigned metric of uncertainty. In this study, we apply Bayesian networks to build on prior feature selection analyses and construct easy-to-interpret probabilistic models, which accurately predict levels of dissolved organic carbon (DOC) from the relative abundance of soil bacteria (16S rRNA gene profiles). In addition to standard cross-validation, we show that a Bayesian network model trained using samples from a pine litter decomposition study accurately predicts DOC of samples from an independent oak litter decomposition study, suggesting that mechanisms driving variation in soil carbon storage may be conserved across different types of decomposing plant litter. Furthermore, the structure of the resulting Bayesian network model defines a minimal set of highly informative taxa, whose abundances directly constrain the probability of high or low DOC conditions. Significant accuracy of the Bayesian network model with independent data sets supports the validity of the identified relationships between taxa abundance and DOC.
Sequential sequestrations increase the incorporation and retention of multiple growth factors in mineralized collagen scaffolds.

Trauma induced injuries of the mouth, jaw, face, and related structures present unique clinical challenges due to their large size and complex geometry. Growth factor signaling coordinates the behavior of multiple cell types following an injury, and effective coordination of growth factor availability within a biomaterial can be critical for accelerating bone healing. Mineralized collagen scaffolds are a class of degradable biomaterial whose biophysical and compositional parameters can be adjusted to facilitate cell invasion and tissue remodeling. Here we describe the use of modified simulated body fluid treatments to enable sequential sequestration of bone morphogenic protein 2 and vascular endothelial growth factor into mineralized collagen scaffolds for bone repair. We report the capability of these scaffolds to sequester 60–90% of growth factor from solution without additional crosslinking treatments and show high levels of retention for individual (>94%) and multiple growth factors (>88%) that can be layered into the material via sequential sequestration steps. Sequentially sequestering growth factors allows prolonged release of growth factors in vitro (>94%) and suggests the potential to improve healing of large-scale bone injury models in vivo. Future work will utilize this sequestration method to induce cellular activities critical to bone healing such as vessel formation and cell migration.