cssi/SciSciGPT-SciSciNet huggingface dataset:
Digital cellular solid pressure vessels: A novel approach for human habitation in space

It is widely assumed that human exploration beyond Earths orbit will require vehicles capable of providing long-duration habitats that simulate an Earthlike environment — consistent artificial gravity, breathable atmosphere, and sufficient living space-while requiring the minimum possible launch mass. This paper examines how the qualities of digital cellular solids — high-performance, repairability, reconfigurability, tunable mechanical response — allow the accomplishment of long-duration habitat objectives at a fraction of the mass required for traditional structural technologies. To illustrate the impact digital cellular solids could make as a replacement to conventional habitat subsystems, we compare recent proposed deep space habitat structural systems with a digital cellular solids pressure vessel design that consists of a carbon fiber reinforced polymer (CFRP) digital cellular solid cylindrical framework that is lined with an ultra-high molecular weight polyethylene (UHMWPE) skin. We use the analytical treatment of a linear specific modulus scaling cellular solid to find the minimum mass pressure vessel for a structure and find that, for equivalent habitable volume and appropriate safety factors, the use of digital cellular solids provides clear methods for producing structures that are not only repairable and reconfig-urable, but also higher performance than their conventionally-manufactured counterparts.
Clark theory in the Drury–Arveson space ☆

Abstract We extend the basic elements of Clark's theory of rank-one perturbations of backward shifts, to row-contractive operators associated to de Branges–Rovnyak type spaces H ( b ) contrastively contained in the Drury–Arveson space on the unit ball in C d . The Aleksandrov–Clark measures on the circle are replaced by a family of states on a certain noncommutative operator system, and the backward shift is replaced by a canonical solution to the Gleason problem in H ( b ) . In addition we introduce the notion of a “quasi-extreme” multiplier of the Drury–Arveson space and use it to characterize those H ( b ) spaces that are invariant under multiplication by the coordinate functions.
Rats optimally accumulate and discount evidence in a dynamic environment

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.
Variance-based rewards for approximate Bayesian reinforcement learning

The explore-exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over environments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, results in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninformative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task.
Global unscented attitude estimation via the matrix Fisher distributions on SO(3)

This paper is focused on probabilistic estimation for the attitude dynamics of a rigid body on the special orthogonal group. We select the matrix Fisher distribution to represent the uncertainties of attitude estimates and measurements in a global fashion without need for local coordinates. Several properties of the matrix Fisher distribution on the special orthogonal group are presented, and an unscented transform is proposed to approximate a matrix Fisher distribution by selected sigma points. Based on these, an intrinsic, global framework for Bayesian attitude estimation is developed. It is shown that the proposed approach can successfully deal with large initial estimator errors and large uncertainties over complex maneuvers to obtain accurate estimates of the attitude.
On the residual finiteness growths of particular hyperbolic manifold groups

We give a quantification of residual finiteness for the fundamental groups of hyperbolic manifolds that admit a totally geodesic immersion to a compact, right-angled Coxeter orbifold of dimension 3 or 4. Specifically, we give explicit upper bounds on residual finiteness that are linear in terms of geodesic length. We then extend the linear upper bounds to hyperbolic manifolds with a finite cover that admits such an immersion. Since the quantifications are given in terms of geodesic length, we define the geodesic residual finiteness growth and show that this growth is equivalent to the usual residual finiteness growth defined in terms of word length. This equivalence implies that our results recover the quantification of residual finiteness from Bou-Rabee et al. (Math Z, arXiv:1402.6974 [math.GR]) for hyperbolic manifolds that virtually immerse into a compact reflection orbifold.
The Parisi ultrametricity conjecture

In this paper we prove that the support of a random measure on the unit ball of a separable Hilbert space that satises the Ghirlanda-Guerra identities must be ultrametric with probability one. This implies the Parisi ultrametricity conjecture in mean-eld spin glass models, such as the Sherrington-Kirkpatrick and mixed p-spin models, for which Gibbs measures are known to satisfy the Ghirlanda-Guerra identities in the thermodynamic limit.
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph

Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases. We present a method for performing deductive reasoning directly in such a vector space, combining analogy, association, and deduction in a straightforward way at each step in a chain of reasoning, drawing on knowledge from diverse sources and ontologies.
A Unified View of Localized Kernel Learning.

Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions. Most MKL methods seek the combined kernel that performs best over every training example, sacrificing performance in some areas to seek a global optimum. Localized kernel learning (LKL) overcomes this limitation by allowing the training algorithm to match a component kernel to the examples that can exploit it best. Several approaches to the localized kernel learning problem have been explored in the last several years. We unify many of these approaches under one simple system and design a new algorithm with improved performance. We also develop enhanced versions of existing algorithms, with an eye on scalability and performance.
Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach

In this paper, the problem of energy trading between smart grid prosumers, who can simultaneously consume and produce energy, and a grid power company is studied. The problem is formulated as a single-leader, multiple-follower Stackelberg game between the power company and multiple prosumers. In this game, the power company acts as a leader who determines the pricing strategy that maximizes its profits, while the prosumers act as followers who react by choosing the amount of energy to buy or sell so as to optimize their current and future profits. The proposed game accounts for each prosumer’s subjective decision when faced with the uncertainty of profits, induced by the random future price. In particular, the framing effect, from the framework of prospect theory (PT), is used to account for each prosumer’s valuation of its gains and losses with respect to an individual utility reference point. The reference point changes between prosumers and stems from their past experience and future aspirations of profits. The followers’ noncooperative game is shown to admit a unique pure-strategy Nash equilibrium (NE) under classical game theory which is obtained using a fully distributed algorithm. The results are extended to account for the case of PT using algorithmic solutions that can achieve an NE under certain conditions. Simulation results show that the total grid load varies significantly with the prosumers’ reference point and their loss-aversion level. In addition, it is shown that the power company’s profits considerably decrease when it fails to account for the prosumers’ subjective perceptions under PT.
Partial information framework: Model-based aggregation of estimates from diverse information sources

Prediction polling is an increasingly popular form of crowdsourcing in which multiple participants estimate the probability or magnitude of some future event. These estimates are then aggregated into a single forecast. Historically, randomness in scientific estimation has been generally assumed to arise from unmeasured factors which are viewed as measurement noise. However, when combining subjective estimates, heterogeneity stemming from differences in the participants’ information is often more important than measurement noise. This paper formalizes information diversity as an alternative source of such heterogeneity and introduces a novel modeling framework that is particularly well-suited for prediction polls. A practical specification of this framework is proposed and applied to the task of aggregating probability and point estimates from two real-world prediction polls. In both cases our model outperforms standard measurement-error-based aggregators, hence providing evidence in favor of information diversity being the more important source of heterogeneity.
Transcranial focused ultrasound for BOLD fMRI signal modulation in humans

Transcranial focused ultrasound (tFUS) is an emerging form of non-surgical human neuromodulation that confers advantages over existing electro and electromagnetic technologies by providing a superior spatial resolution on the millimeter scale as well as the capability to target sub-cortical structures non-invasively. An examination of the pairing of tFUS and blood oxygen level dependent (BOLD) functional MRI (fMRI) in humans is presented here.
Capacity Theorems for Broadcast Channels With Two Channel State Components Known at the Receivers

We establish the capacity region of several classes of broadcast channels with random state in which the channel to each user is selected from two possible channel state components and the state is known only at the receivers. When the channel components are deterministic, we show that the capacity region is achieved via Marton coding. This channel model does not belong to any class of broadcast channels for which the capacity region was previously known and is useful in studying wireless communication channels when the fading state is known only at the receivers. We then establish the capacity region when the channel components are ordered, e.g., degraded. In particular, we show that the capacity region for the broadcast channel with degraded Gaussian vector channel components is attained via Gaussian input distribution. Finally, we extend the results on ordered channels to two broadcast channel examples with more than two channel components, but show that these extensions do not hold in general.
Q-learning with Nearest Neighbors

We consider model-free reinforcement learning for infinite-horizon discounted Markov Decision Processes (MDPs) with a continuous state space and unknown transition kernel, when only a single sample path under an arbitrary policy of the system is available. We consider the Nearest Neighbor Q-Learning (NNQL) algorithm to learn the optimal Q function using nearest neighbor regression method. As the main contribution, we provide tight finite sample analysis of the convergence rate. In particular, for MDPs with a $d$-dimensional state space and the discounted factor $\gamma \in (0,1)$, given an arbitrary sample path with ``covering time'' $L$, we establish that the algorithm is guaranteed to output an $\varepsilon$-accurate estimate of the optimal Q-function using $\Ot(L/(\varepsilon^3(1-\gamma)^7))$ samples. For instance, for a well-behaved MDP, the covering time of the sample path under the purely random policy scales as $\Ot(1/\varepsilon^d),$ so the sample complexity scales as $\Ot(1/\varepsilon^{d+3}).$ Indeed, we establish a lower bound that argues that the dependence of $ \Omegat(1/\varepsilon^{d+2})$ is necessary.
Stability and control of ad hoc dc microgrids

Ad hoc electrical networks are formed by connecting power sources and loads without pre-determining the network topology. These systems are well-suited to addressing the lack of electricity in rural areas because they can be assembled and modified by non-expert users without central oversight. There are two core aspects to ad hoc system design: 1) designing source and load units such that the microgrid formed from the arbitrary interconnection of many units is always stable and 2) developing control strategies to autonomously manage the microgrid (i.e., perform power dispatch and voltage regulation) in a decentralized manner and under large uncertainty. To address these challenges we apply a number of nonlinear control techniques—including Brayton-Moser potential theory and primal-dual dynamics—to obtain conditions under which an ad hoc dc microgrid will have a suitable and asymptotically stable equilibrium point. Further, we propose a new decentralized control scheme that coordinates many sources to achieve a specified power dispatch from each. A simulated comparison to previous research is included.
Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks

This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planning for evacuations, robotic navigations, and path planning for autonomous vehicles. We propose an architecture based on GANs to recommend accurate and reliable paths for navigation applications. The proposed system can use crowd-sourced data to learn the trajectories and infer new ones. The system provides users with generated paths that help them navigate from their local environment to reach a desired location. As a use case, we experimented with the proposed method in support of a wayfinding application in an indoor environment. Our experiments assert that the generated paths are correct and reliable. The accuracy of the classification task for the generated paths is up to 99% and the quality of the generated paths has a mean opinion score of 89%.
Relative categories: Another model for the homotopy theory of homotopy theories

Abstract We lift Charles Rezk’s complete Segal space model structure on the category of simplicial spaces to a Quillen equivalent one on the category of relative categories.
Dynamic product assembly and inventory control for maximum profit

We consider a manufacturing plant that purchases raw materials for product assembly and then sells the final products to customers. There are M types of raw materials and K types of products, and each product uses a certain subset of raw materials for assembly. The plant operates in slotted time, and every slot it makes decisions about re-stocking materials and pricing the existing products in reaction to (possibly time-varying) material costs and consumer demands. We develop a dynamic purchasing and pricing policy that yields time average profit within ∈ of optimality, for any given ∈ > 0, with a worst case storage buffer requirement that is O(1/∈). The policy can be implemented easily for large M, K, yields fast convergence times, and is robust to non-ergodic system dynamics.
Witt, GW, K-theory of quasi-projective schemes

Abstract In this article, we prove some results on Witt, Grothendieck–Witt (GW) and K-theory of noetherian quasi-projective schemes X, over affine schemes Spec ( A ) . For integers k ≥ 0 , let C M k ( X ) denote the category of coherent O X -modules F , with locally free dimension dim V ( X ) ⁡ ( F ) = k = grade ( F ) . We prove that there is an equivalence D b ( C M k ( X ) ) → D k ( V ( X ) ) of the derived categories. It follows that there is a sequence of zig-zag maps K ( C M k + 1 ( X ) ) ⟶ K ( C M k ( X ) ) ⟶ ∐ x ∈ X ( k ) K ( C M k ( X x ) ) of the K -theory spectra that is a homotopy fibration. In fact, this is analogous to the homotopy fiber sequence of the G-theory spaces of Quillen (see proof of [16, Theorem 5.4] ). We also establish similar homotopy fibrations of GW-spectra and G W -bispectra, by application of the same equivalence theorem.
On the dimension of a certain measure in the plane

where dν denotes two dimensional Lebesgue measure. The space W 1,p loc (Ω) is defined in the obvious manner; h ∈ W 1,p loc (Ω) if and only if h ∈ W (U) for every open U ⋐ Ω, i.e., compactly contained in Ω. Let C 0 (Ω ) denote infinitely differentiable functions with compact support in Ω and let W 1,p 0 (Ω ) denote the closure of C 0 (Ω ) in the norm of W (Ω). Let 〈·, ·〉 denote the standard inner product on C. Fix p, 1 < p < ∞ and let f : C \ {0} → (0,∞) be homogeneous of degree p on C \ {0}. That is, f(η) = |η|f (
Interpolation of Varieties of Minimal Degree

It is well known that one can find a rational normal curve in $\mathbb P^n$ through $n+3$ general points. We prove a generalization of this to higher dimensional varieties, showing that smooth varieties of minimal degree can be interpolated through points and linear spaces.
DeepSF: Deep Convolutional Neural Network for Mapping Protein Sequences to Folds

Predicting the fold of a protein from its sequence is a challenging and important problem in bioinformatics. In this work, we developed the first deep learning method that can directly classify any protein sequence of arbitrary length into 1,195 known protein folds in the Structural Classification of Proteins (SCOP) database. The method uses one-dimensional convolutional neural networks (1D-CNN) to automatically extract features from raw protein sequence information and compose them to high-level features to predict protein folds, achieving a classification accuracy of 73% on an independent test dataset. This novel machine learning approach is different from traditional alignment methods that rely on the known folds of homologous proteins to recognize the fold of a target protein. It is also more accurate than HHSearch - a top protein profile-profile alignment method in recognizing folds of proteins that have little sequence similarity with proteins with known folds. Moreover, the deep learning method overcomes the shortcoming of the previous machine learning methods that can only classify proteins into dozens of pre-selected folds due to methodological limitations. The hidden features extracted by the deep learning method provide a new semantic representation of proteins that can be used in other protein analysis tasks such as protein clustering and comparison.
Positivity for cluster algebras from surfaces

Abstract We give combinatorial formulas for the Laurent expansion of any cluster variable in any cluster algebra coming from a triangulated surface (with or without punctures), with respect to an arbitrary seed. Moreover, we work in the generality of principal coefficients . An immediate corollary of our formulas is a proof of the positivity conjecture of Fomin and Zelevinsky for cluster algebras from surfaces, in geometric type.
A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution.

Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor data quality, ER is a challenging task for any automated process. As a remedy, human-powered ER via crowdsourcing has become popular in recent years. Using crowd to answer queries is costly and time consuming. Furthermore, crowd-answers can often be faulty. Therefore, crowd-based ER methods aim to minimize human participation without sacrificing the quality and use a computer generated similarity matrix actively. While, some of these methods perform well in practice, no theoretical analysis exists for them, and further their worst case performances do not reflect the experimental findings. This creates a disparity in the understanding of the popular heuristics for this problem. In this paper, we make the first attempt to close this gap. We provide a thorough analysis of the prominent heuristic algorithms for crowd-based ER. We justify experimental observations with our analysis and information theoretic lower bounds.
Efficient time stepping for reactive turbulent simulations with stiff chemistry

A combination of a steady-state preserving operator splitting method and a semi-implicit integration scheme is proposed for efficient time stepping in simulations of unsteady reacting flows, such as turbulent flames, using detailed chemical kinetic mechanisms. The operator splitting is based on the Simpler balanced splitting method, which is constructed with improved stability properties and reduced computational cost. The method is shown to be capable of stable and accurate prediction of ignition and extinction for reaction-diffusion systems near critical conditions. The ROK4E scheme is designed for semi-implicit integration of spatially independent chemically reacting systems. Being a Rosenbrock-Krylov method, ROK4E utilizes the low-rank approximation of the Jacobian to reduce the cost for integrating the system of ODEs that have relative few stiff components. The efficiency of the scheme is further improved via the careful choice of coefficients to require three right-hand-side evaluations over four stages. Combing these two methods, efficient calculation is achieved for large-scale parallel simulations of turbulent flames.
Data Dropout in Arbitrary Basis for Deep Network Regularization

In training deep networks with high capacity, it is important to ensure that the trained network works well when presented with new inputs outside the training dataset. Dropout is an effective regularization technique to boost the network’s generalization in which a random subset of the elements of the given data and the extracted features are set to zero during the training process. In this paper, a new randomized regularization technique in which we withhold a random part of the data without necessarily turning off the neurons/data-elements is proposed. In the proposed method, of which the conventional dropout is shown to be a special case, random data dropout is performed in an arbitrary basis, hence the designation Generalized Dropout. We also present a framework whereby the proposed technique can be applied efficiently to convolutional neural networks. The presented numerical experiments demonstrate that the proposed technique yields a notable performance gain. Generalized Dropout provides new insight into the idea of dropout, shows that we can achieve different performance gains by using different bases matrices, and opens up a new research question as of how to choose optimal bases matrices that achieve maximal performance gain.
Blind Gain and Phase Calibration via Sparse Spectral Methods

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the ill-posedness of BGPC. Recently, there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature- based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signals. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection rate, false alarm, and accuracy. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.
Set families with forbidden subposets

Abstract Let F be a family of subsets of { 1 , … , n } . We say that F is P-free if the inclusion order on F does not contain P as an induced subposet. The Turan function of P, denoted La ⁎ ( n , P ) , is the maximum size of a P-free family of subsets of { 1 , … , n } . We show that La ⁎ ( n , P ) ≤ ( 4 r + O ( r ) ) ( n ⌊ n / 2 ⌋ ) if P is an r-element poset of height at most 2. We also show that La ⁎ ( n , S r ) = ( r + O ( r ) ) ( n ⌊ n / 2 ⌋ ) where S r is the standard example on 2r elements, and that La ⁎ ( n , 2 [ 2 ] ) ≤ ( 2.583 + o ( 1 ) ) ( n ⌊ n / 2 ⌋ ) , where 2 [ 2 ] is the 2-dimensional Boolean lattice.
Reconstruction of Coefficients in Scalar Second-Order Elliptic Equations from Knowledge of Their Solutions

This paper concerns the reconstruction of possibly complex-valued coefficients in a second-order scalar elliptic equation that is posed on a bounded domain from knowledge of several solutions of that equation. We show that for a sufficiently large number of solutions and for an open set of corresponding boundary conditions, all coefficients can be uniquely and stably reconstructed up to a well-characterized gauge transformation. We also show that in some specific situations, a minimum number of such available solutions equal to is sufficient to uniquely and globally reconstruct the unknown coefficients. This theory finds applications in several coupled-physics medical imaging modalities including photo-acoustic tomography, transient elastography, and magnetic resonance elastography. © 2013 Wiley Periodicals, Inc.
Spectral Signatures in Backdoor Attacks

A recent line of work has uncovered a new form of data poisoning: so-called backdoor attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by an adversary's planted perturbation. In this paper, we identify a new property of all known backdoor attacks, which we call spectral signatures. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards a principled understanding of backdoor attacks.
Asymptotic Stability for the Couette Flow in the 2D Euler Equations

In this expository note we discuss our recent work [arXiv:1306.5028] on the nonlinear asymptotic stability of shear flows in the 2D Euler equations of ideal, incompressible flow. In that work it is proved that perturbations to the Couette flow which are small in a suitable regularity class converge strongly in $L^2$ to a shear flow which is close to the Couette flow. Enstrophy is mixed to small scales by an almost linear evolution and is generally lost in the weak limit as t -> +/- infinity. In this note we discuss the most important physical and mathematical aspects of the result and the key ideas of the proof.
A CAT(0)-valued pointwise ergodic theorem

This note proves a version of the pointwise ergodic theorem for functions taking values in a separable complete CAT(0)-space. The precise setting consists of an amenable locally compact group G with left Haar measure mG, a jointly measurable, probability-preserving action $T : G \curvearrowright (\Omega,\mathcal{F},\mathsf{P})$ of G on a probability space, and a separable complete CAT(0)-space (X, d) with barycentre map b. In this setting we show that if (Fn)n ≥ 1 is a tempered Folner sequence of compact subsets of G and f : Ω → X is a measurable map such that for some (and hence any) fixed x ∈ X, we have \[ \int_\Omega d(f(\omega),x)^2\,\mathsf{P}(\mathrm{d} \omega) < \infty, \] then as n → ∞ the functions of empirical barycentres \[ \omega \mapsto b\Big(\frac{1}{m_G(F_n)}\int_{F_n}\delta_f({T}^g\omega)\,m_G(\mathrm{d} g)\Big) \] converge pointwise for almost every ω to a T-invariant function $\bar{f}:\Omega \to X$.
LifeRaft: Data-Driven, Batch Processing for the Exploration of Scientific Databases.

Workloads that comb through vast amounts of data are gaining importance in the sciences. These workloads consist of ineedle in a haystacki queries that are long running and data intensive so that query throughput limits performance. To maximize throughput for data-intensive queries, we put forth LifeRaft: a query processing system that batches queries with overlapping data requirements. Rather than scheduling queries in arrival order, LifeRaft executes queries concurrently against an ordering of the data that maximizes data sharing among queries. This decreases I/O and increases cache utility. However, such batch processing can increase query response time by starving interactive workloads. LifeRaft addresses starvation using techniques inspired by head scheduling in disk drives. Depending upon the workload saturation and queuing times, the system adaptively and incrementally trades-off processing queries in arrival order and data-driven batch processing. Evaluating LifeRaft in the SkyQuery federation of astronomy databases reveals a two-fold improvement in query throughput.
Stabilization of a Rigid Body Payload With Multiple Cooperative Quadrotors

This paper presents the full dynamics and control of arbitrary number of quadrotor unmanned aerial vehicles (UAV) transporting a rigid body. The rigid body is connected to the quadrotors via flexible cables where each flexible cable is modeled as a system of arbitrary number of serially connected links. It is shown that a coordinate-free form of equations of motion can be derived for the complete model without any simplicity assumptions that commonly appear in other literature, according to Lagrangian mechanics on a manifold. A geometric nonlinear controller is presented to transport the rigid body to a fixed desired position while aligning all of the links along the vertical direction. A rigorous mathematical stability proof is given and the desirable features of the proposed controller are illustrated by numerical examples and experimental results.
Conservation, inertia, and spacetime geometry

As Harvey Brown emphasizes in his book Physical Relativity, inertial motion in general relativity is best understood as a theorem, and not a postulate. Here I discuss the status of the "conservation condition", which states that the energy-momentum tensor associated with non-interacting matter is covariantly divergence-free, in connection with such theorems. I argue that the conservation condition is best understood as a consequence of the differential equations governing the evolution of matter in general relativity and many other theories. I conclude by discussing what it means to posit a certain spacetime geometry and the relationship between that geometry and the dynamical properties of matter.
Consistency of Bayesian linear model selection with a growing number of parameters

Abstract Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which provide a stochastic search of informative variables, have gained popularity. In this paper, we will study the asymptotic properties related to Bayesian model selection when the model dimension p is growing with the sample size n. We consider p ≤ n and provide sufficient conditions under which: (1) with large probability, the posterior probability of the true model (from which samples are drawn) uniformly dominates the posterior probability of any incorrect models; and (2) the posterior probability of the true model converges to one in probability. Both (1) and (2) guarantee that the true model will be selected under a Bayesian framework. We also demonstrate several situations when (1) holds but (2) fails, which illustrates the difference between these two properties. Finally, we generalize our results to include g-priors, and provide simulation examples to illustrate the main results.
MengeROS: A Crowd Simulation Tool for Autonomous Robot Navigation.

While effective navigation in large, crowded environments is essential for an autonomous robot, preliminary testing of algorithms to support it requires simulation across a broad range of crowd scenarios. Most available simulation tools provide either realistic crowds without robots or realistic robots without realistic crowds. This paper introduces MengeROS, a 2-D simulator that realistically integrates multiple robots and crowds. MengeROS provides a broad range of settings in which to test the capabilities and performance of navigation algorithms designed for large crowded environments.
Hypersampling of pseudo-periodic signals by analytic phase projection.

Abstract A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This “hypersampling” requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a considerably higher effective sampling rate than the time series. The procedure is applied to time series of MRI images of the human brain. As a result, the effective sampling rate could be increased by three orders of magnitude. This allows for capturing the waveforms of the very fast cerebral pulse waves traversing the brain. Hypersampling is numerically compared to the more commonly used retrospective gating. An outlook regarding EEG and optical recordings of brain activity as the reference signal is provided.
Bayesian Fusion Estimation via t Shrinkage

Shrinkage prior has gained great successes in many data analysis, however, its applications mostly focus on the Bayesian modeling of sparse parameters. In this work, we will apply Bayesian shrinkage to model high dimensional parameter that possesses an unknown blocking structure. We propose to impose heavy-tail shrinkage prior, e.g., t prior, on the differences of successive parameter entries, and such a fusion prior will shrink successive differences towards zero and hence induce posterior blocking. Comparing to conventional Bayesian fused LASSO which implements Laplace fusion prior, t fusion prior induces stronger shrinkage effect and enjoys a nice posterior consistency property. Simulation studies and real data analyses show that t fusion has superior performance to the frequentist fusion estimator and Bayesian Laplace fusion prior. This t fusion strategy is further developed to conduct a Bayesian clustering analysis, and our simulations show that the proposed algorithm compares favorably to classical Dirichlet process modeling.
Precisely Control Mitochondria with Light to Manipulate Cell Fate Decision

Mitochondrial dysfunction has been implicated in many pathological conditions and diseases. The normal functioning of mitochondria relies on maintaining the inner mitochondrial membrane potential (also known as ΔΨm) that is essential for ATP synthesis, Ca2+ homeostasis, redox balance, and regulation of other key signaling pathways such as mitophagy and apoptosis. However, the detailed mechanisms by which ΔΨm regulates cellular function remain incompletely understood, partially because of the difficulty of manipulating ΔΨm with spatiotemporal resolution, reversibility, or cell type specificity. To address this need, we have developed a next generation optogenetic-based technique for controllable mitochondrial depolarization with light. We demonstrate successful targeting of the heterologous channelrhodopsin-2 fusion protein to the inner mitochondrial membrane and formation of functional cationic channels capable of light-induced selective ΔΨm depolarization and mitochondrial autophagy. Importantly, we for the first time, to our knowledge, show that optogenetic-mediated mitochondrial depolarization can be well controlled to differentially influence the fate of cells expressing mitochondrial channelrhodopsin-2; whereas sustained moderate light illumination induces substantial apoptotic cell death, transient mild light illumination elicits cytoprotection via mitochondrial preconditioning. Finally, we show that Parkin overexpression exacerbates, instead of ameliorating, mitochondrial depolarization-mediated cell death in HeLa cells. In summary, we provide evidence that the described mitochondrial-targeted optogenetics may have a broad application for studying the role of mitochondria in regulating cell function and fate decision.
A Polyhedral Model for Enumeration and Optimization over the Set of Circuits

Abstract Circuits play a fundamental role in polyhedral theory and linear programming. For instance, circuits are used as step directions in various augmentation schemes for solving linear programs or to leave degenerate vertices while running the simplex method. However, there are significant challenges when implementing these approaches: The set of circuits of a polyhedron may be of exponential size and is highly sensitive to the representation of the polyhedron. In this paper, we provide a universal framework for enumerating the set of circuits and optimizing over sets of circuits of a polyhedron in any representation—we propose a polyhedral model in which the circuits of the original polyhedron are encoded as extreme rays or vertices. Many methods in the literature and software assume that a polyhedron is in standard form; our framework is a direct generalization. We demonstrate its value by showing that the conversion of a general representation to standard form may introduce exponentially many new circuits. We then discuss the main advantages of the generalized polyhedral model. It enables the direct enumeration of useful subsets of circuits such as strictly feasible circuits or sign-compatible circuits, as well as optimization over these sets. In particular, this leads to the efficient computation of a steepest-descent circuit, which can be used in an augmentation scheme for solving linear programs or the construction of sign-compatible circuit walks with additional properties.
Prospective assessment of demographic characteristics associated with worse health related quality of life measures following definitive chemoradiation in patients with locally advanced non-small cell lung cancer.

Background The purpose of this study was to evaluate baseline demographic characteristics which may be associated with worse health related quality of life (HRQOL) for patients with locally advanced non-small cell lung cancer (NSCLC) receiving definitive chemoradiation (CRT). Materials Patients with NSCLC were prospectively enrolled on an Institutional Review Board-approved clinical trial between 2009 and 2012. HRQOL assessments were collected pre-radiation therapy (RT), during RT, and within 3 months post-RT using Euroqol (EQ-5D), MD Anderson Symptom Inventory (MDASI), and Functional Assessment of Cancer Therapy General (FACT-G). HRQOL correlation was assessed with categorical variables by Wilcoxon rank sum tests and with continuous variables by Pearson correlation. P<0.05 was defined as statistically significant. Results Forty-three consecutive patients received definitive concurrent CRT and completed assessments at one or more time-points. Patients most commonly had stage IIIB disease (72%), were married or with a partner (70%) and Caucasian (91%). Median patient age was 65 (range: 39-79) years and Charlson comorbidity index (CCI) was 0 (range: 0-5). Female gender, African-American ethnicity, age, chemotherapy type, baseline hemoglobin, and CCI were associated with worse post-treatment HRQOL measures. Conclusions We have identified novel characteristics associated with worse quality of life following definitive CRT for lung cancer. Patients at risk for worse post-treatment quality of life may benefit from earlier follow-up and greater supportive measures following treatment.
Quantitatively Partitioning Microbial Genomic Traits among Taxonomic Ranks across the Microbial Tree of Life

ABSTRACT Widely used microbial taxonomies, such as the NCBI taxonomy, are based on a combination of sequence homology among conserved genes and historically accepted taxonomies, which were developed based on observable traits such as morphology and physiology. A recently proposed alternative taxonomy database, the Genome Taxonomy Database (GTDB), incorporates only sequence homology of conserved genes and attempts to partition taxonomic ranks such that each rank implies the same amount of evolutionary distance, regardless of its position on the phylogenetic tree. This provides the first opportunity to completely separate taxonomy from traits and therefore to quantify how taxonomic rank corresponds to traits across the microbial tree of life. We quantified the relative abundances of clusters of orthologous group functional categories (COG-FCs) as a proxy for traits within the lineages of 13,735 cultured and uncultured microbial lineages from a custom-curated genome database. On average, 41.4% of the variation in COG-FC relative abundance is explained by taxonomic rank, with domain, phylum, class, order, family, and genus explaining, on average, 3.2%, 14.6%, 4.1%, 9.2%, 4.8%, and 5.5% of the variance, respectively (P IMPORTANCE Recently, there has been great progress in defining a complete taxonomy of bacteria and archaea, which has been enabled by improvements in DNA sequencing technology and new bioinformatic techniques. A new, algorithmically defined microbial tree of life describes those linkages, relying solely on genetic data, which raises the issue of how microbial traits relate to taxonomy. Here, we adopted cluster of orthologous group functional categories as a scheme to describe the genomic contents of microbes, a method that can be applied to any microbial lineage for which genomes are available. This simple approach allows quantitative comparisons between microbial genomes with different gene compositions from across the microbial tree of life. Our observations demonstrate statistically significant patterns in cluster of orthologous group functional categories at taxonomic levels that span the range from domain to genus.
Bordered knot algebras with matchings

This paper generalizes the bordered-algebraic knot invariant introduced in an earlier paper, giving an invariant now with more algebraic structure. It also introduces signs to define these invariants with integral coefficients. We describe effective computations of the resulting invariant.
Pulser: Fast Congestion Response Using Explicit Incast Notifications for Datacenter Networks

Datacenter applications frequently cause incast congestion, which degrades short flows’ flow completion times and long flows’ throughput. Existing congestion control schemes (e.g., DCTCP) do not explicitly detect and isolate incast. Instead, they rely on existing Explicit Congestion Notification (ECN) to react to general congestion. They, therefore, lose performance due to slow, cautious, and inaccurate reaction to incast. We propose a novel algorithm that detects incasts and notifies senders using a new Explicit Incast Notification EIN). We show that our incast detection is fast and accurate. Next, we present our congestion control scheme, called Pulser , which isolates incasts using EIN. Unlike DCTCP, which gradually adjusts sending rate, Pulser drastically backs off during incast and rapidly restores sending rate once incast ends (i.e., like a pulse). Our real experiments and ns-3 simulations show that Pulser outperforms prior schemes, DCTCP and ICTCP, in both flow completion times and throughput.