cssi/SciSciGPT-SciSciNet huggingface dataset:
Progressive neural networks for transfer learning in emotion recognition

Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another. In this paper, we investigate how knowledge can be transferred between three paralinguistic tasks: speaker, emotion, and gender recognition. Further, we extend this problem to cross-dataset tasks, asking how knowledge captured in one emotion dataset can be transferred to another. We focus on progressive neural networks and compare these networks to the conventional deep learning method of pre-training and fine-tuning. Progressive neural networks provide a way to transfer knowledge and avoid the forgetting effect present when pre-training neural networks on different tasks. Our experiments demonstrate that: (1) emotion recognition can benefit from using representations originally learned for different paralinguistic tasks and (2) transfer learning can effectively leverage additional datasets to improve the performance of emotion recognition systems.
Decoupling Strategy and Generation in Negotiation Dialogues

We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just $50!”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.
Hessian-based sampling for high-dimensional model reduction

In this work we develop a Hessian-based sampling method for the construction of goal-oriented reduced order models with high-dimensional parameter inputs. Model reduction is known very challenging for high-dimensional parametric problems whose solutions also live in high-dimensional manifolds. However, the manifold of some quantity of interest (QoI) depending on the parametric solutions may be low-dimensional. We use the Hessian of the QoI with respect to the parameter to detect this low-dimensionality, and draw training samples by projecting the high-dimensional parameter to a low-dimensional subspace spanned by the eigenvectors of the Hessian corresponding to its dominating eigenvalues. Instead of forming the full Hessian, which is computationally intractable for a high-dimensional parameter, we employ a randomized algorithm to efficiently compute the dominating eigenpairs of the Hessian whose cost does not depend on the nominal dimension of the parameter but only on the intrinsic dimension of the QoI. We demonstrate that the Hessian-based sampling leads to much smaller errors of the reduced basis approximation for the QoI compared to a random sampling for a diffusion equation with random input obeying either uniform or Gaussian distributions.
Matrix Continued Fraction Solution to the Relativistic Spin-0 Feshbach-Villars Equations

The Feshbach–Villars equations, like the Klein–Gordon equation, are relativistic quantum mechanical equations for spin-0 particles.We write the Feshbach–Villars equations into an integral equation form and solve them by applying the Coulomb–Sturmian potential separable expansion method. We consider boundstate problems in a Coulomb plus short range potential. The corresponding Feshbach–Villars CoulombGreen’s operator is represented by a matrix continued fraction.
A STEADY-STATE ALIGNMENT FRONT IN AN ACCRETION DISK SUBJECTED TO LENSE–THIRRING TORQUES

Using only physical mechanisms, i.e., 3D magnetohydrodynamics (MHD) with no phenomenological viscosity, we have simulated the dynamics of a moderately thin accretion disk subject to torques whose radial scaling mimics those produced by lowest-order post-Newtonian gravitomagnetism. In this simulation, we have shown how, in the presence of MHD turbulence, a time-steady transition can be achieved between an inner disk region aligned with the equatorial plane of the central mass?s spin and an outer region orbiting in a different plane. The position of the equilibrium orientation transition is determined by a balance between gravitomagnetic torque and warp-induced inward mixing of misaligned angular momentum from the outer disk. If the mixing is interpreted in terms of diffusive transport, the implied diffusion coefficient is ?(0.6?0.8) for sound speed cs and orbital frequency ?. This calibration permits estimation of the orientation transition?s equilibrium location given the central mass, its spin parameter, and the disk?s surface density and scaleheight profiles. However, the alignment front overshoots before settling into an equilibrium, signaling that a diffusive model does not fully represent the time-dependent properties of alignment fronts under these conditions. Because the precessional torque on the disk at the alignment front is always comparable to the rate at which misaligned angular momentum is brought inward to the front by warp-driven radial motions, no break forms between the inner and outer portions of the disk in our simulation. Our results also raise questions about the applicability to MHD warped disks of the traditional distinction between ?bending wave? and ?diffusive? regimes.
Hardware transactional persistent memory

Emerging Persistent Memory technologies (also pm, Non-Volatile DIMMs, Storage Class Memory or scm) hold tremendous promise for accelerating popular data-management applications like in-memory databases. However, programmers now need to deal with ensuring the atomicity of transactions on Persistent Memory resident data and maintaining consistency between the order in which processors perform stores and that in which the updated values become durable. The problem is specially challenging when high-performance isolation mechanisms like Hardware Transactional Memory (htm) are used for concurrency control. This work shows how htm transactions can be ordered correctly and atomically into PM by the use of a novel software protocol combined with a Persistent Memory Controller, without requiring changes to processor cache hardware or htm protocols. In contrast, previous approaches require significant changes to existing processor microarchitectures. Our approach, evaluated using both micro-benchmarks and the stamp suite compares well with standard (volatile) htm transactions. It also yields significant gains in throughput and latency in comparison with persistent transactional locking.
Spiking Neural Algorithms for Markov Process Random Walk

The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by the grid cell spatial representation in the brain. The second method tracks the densities of random walkers at each spatial location directly. We analyze the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions.
Distance closures on complex networks

To expand the toolbox available to network science, we study the isomorphism between distance and Fuzzy (proximity or strength) graphs. Distinct transitive closures in Fuzzy graphs lead to closures of their isomorphic distance graphs with widely different structural properties. For instance, the All Pairs Shortest Paths (APSP) problem, based on the Dijkstra algorithm, is equivalent to a metric closure, which is only one of the possible ways to calculate shortest paths in weighted graphs. We show that different closures lead to different distortions of the original topology of weighted graphs. Therefore, complex network analyses that depend on the calculation of shortest paths on weighted graphs should take into account the closure choice and associated topological distortion. We characterize the isomorphism using the max-min and Dombi disjunction/conjunction pairs. This allows us to: (1) study alternative distance closures, such as those based on diffusion, metric, and ultra-metric distances; (2) identify the operators closest to the metric closure of distance graphs (the APSP), but which are logically consistent; and (3) propose a simple method to compute alternative path length measures and corresponding distance closures using existing algorithms for the APSP. In particular, we show that a specific diffusion distance is promising for community detection in complex networks, and is based on desirable axioms for logical inference or approximate reasoning on networks; it also provides a simple algebraic means to compute diffusion processes on networks. Based on these results, we argue that choosing different distance closures can lead to different conclusions about indirect associations on network data, as well as the structure of complex networks, and are thus important to consider.
An Analytical Framework for Coverage in Cellular Networks Leveraging Vehicles

This paper analyzes an emerging architecture of cellular network utilizing both planar base stations uniformly distributed in the Euclidean plane and base stations located on roads. An example of this architecture is that where, in addition to conventional planar cellular base stations and users, vehicles also play the role of both base stations and users. A Poisson line process is used to model the road network and, conditionally on the lines, linear Poisson point processes are used to model the vehicles on the roads. The conventional planar base stations and users are modeled by the independent planar Poisson point processes. We use Palm calculus to investigate the statistical properties of a typical user in such a network. Specifically, this paper discusses two different Palm distributions, with respect to the user point processes depending on its type: planar or vehicular. We derive the distance to the nearest base station, the association of the typical users, and the coverage probability of the typical user. Furthermore, we provide a comprehensive characterization of coverage of all possible cellular transmissions in this setting, namely, vehicle-to-vehicle, vehicle-to-infrastructure, infrastructure-to-vehicle, and infrastructure-to-infrastructure.
Extended Product and Integrated Interleaved Codes

A new class of codes, Extended Product (EPC) Codes, consisting of a product code with a number of extra parities added, is presented and applications for erasure decoding are discussed. An upper bound on the minimum distance of EPC codes is given, as well as constructions meeting the bound for some relevant cases. A special case of EPC codes, Extended Integrated Interleaved (EII) codes, which naturally unify Integrated Interleaved (II) codes and product codes, is defined and studied in detail. It is shown that EII codes often improve the minimum distance of II codes with the same rate, and they enhance the decoding algorithm by allowing decoding on columns as well as on rows. It is also shown that EII codes allow for encoding II codes with an uniform distribution of the parity symbols.
Targeting and signaling in ad auctions

Modern ad auctions allow advertisers to target more specific segments of the user population. Unfortunately, this is not always in the best interest of the ad platform - partially hiding some information could be more beneficial for the platform's revenue. In this paper, we examine the following basic question in the context of second-price ad auctions: how should an ad platform optimally reveal information about the ad opportunity to the advertisers in order to maximize revenue? We consider a model in which bidders' valuations depend on a random state of the ad opportunity. Different from previous work, we focus on a more practical, and challenging, situation where the space of possible realizations of ad opportunities is extremely large. We thus focus on developing algorithms whose running time is polynomial in the number of bidders, but is independent of the number of ad opportunity realizations. We assume that the auctioneer can commit to a signaling scheme to reveal noisy information about the realized state of the ad opportunity, and examine the auctioneer's algorithmic question of designing the optimal signaling scheme. We first consider that the auctioneer is restricted to send a public signal to all bidders. As a warm-up, we start with a basic (though less realistic) setting in which the auctioneer knows the bidders' valuations, and show that an ϵ-optimal scheme can be implemented in time polynomial in the number of bidders and 1/ϵ. We then move to a well-motivated Bayesian valuation setting in which the auctioneer and bidders both have private information, and present two results. First, we exhibit a characterization result regarding approximately optimal schemes and prove that any constant-approximate public signaling scheme must use exponentially many signals. Second, we present a "simple" public signaling scheme that serves as a constant approximation under mild assumptions. Finally, we initiate an exploration on the power of being able to send different signals privately to different bidders. In the basic setting where the auctioneer knows bidders' valuations, we exhibit a polynomial-time private scheme that extracts almost full surplus even in the worst Bayes Nash equilibrium. This illustrates the surprising power of private signaling schemes in extracting revenue.
Swarm Aggregation under Fading Attractions

Gradient descent methods have been widely used for organizing multi-agent systems, in which they can provide decentralized control laws with provable convergence. Often, the control laws are designed so that two neighboring agents repel/attract each other at a short/long distance of separation. When the interactions between neighboring agents are moreover nonfading, the potential function from which they are derived is radially unbounded. Hence, the LaSalle's principle is sufficient to establish the system convergence. This technical note investigates, in contrast, a more realistic scenario where interactions between neighboring agents have fading attractions. In such setting, the LaSalle type arguments may not be sufficient. To tackle the problem, we introduce a class of partitions, termed dilute partitions , of formations which cluster agents according to the inter- and intra-cluster interaction strengths. We then apply dilute partitions to trajectories of formations generated by the multi-agent system, and show that each of the trajectories remains in a compact subset along the evolution, and converges to the set of equilibria.
Uniform sparse domination of singular integrals via dyadic shifts

Using the Calderon-Zygmund decomposition, we give a novel and simple proof that $L^2$ bounded dyadic shifts admit a domination by positive sparse forms with linear growth in the complexity of the shift. Our estimate, coupled with Hytonen's dyadic representation theorem, upgrades to a positive sparse domination of the class $\mathcal U$ of singular integrals satisfying the assumptions of the classical $T(1)$-theorem of David and Journe, with logarithmic-Dini type smoothness of the integral kernel. Furthermore, our proof extends rather easily to the $\mathbb R^n$-valued case, yielding as a corollary the operator norm bound on the matrix weighted space $L^2(W; \mathbb R^n),$ \[ \left\|T\otimes \mathrm{Id}_{\mathbb R^n}\right\|_{L^2(W; \mathbb R^n)\rightarrow L^2(W; \mathbb R^n)} \lesssim [W]_{A_2}^{\frac32} \] uniformly over $T\in \mathcal U$, which is the currently best known dependence.
Sums-of-squares formulas over algebraically closed fields

Abstract In this paper, we consider whether existence of a sums-of-squares formula depends on the base field. We reformulate the question of existence as a question in algebraic geometry. We show that, for large enough p, existence of sums-of-squares formulas over algebraically closed fields is independent of the characteristic. We make the bound on p explicit, and we prove that the existence of a sums-of-squares formula of fixed type over an algebraically closed field is theoretically (though not practically) computable.
Capacity-achieving codes with bounded graphical complexity on noisy channels

We introduce a new family of concatenated codes with an outer low-density parity-check (LDPC) code and an inner low-density generator matrix (LDGM) code, and prove that these codes can achieve capacity under any memoryless binaryinput output-symmetric (MBIOS) channel using maximum-likelihood (ML) decoding with bounded graphical complexity, i.e., the number of edges per information bit in their graphical representation is bounded. We also show that these codes can achieve capacity for the special case of the binary erasure channel (BEC) under belief propagation (BP) decoding with bounded decoding complexity per information bit for all erasure probabilities in (0, 1). By deriving and analyzing the average weight distribution (AWD) and the corresponding asymptotic growth rate of these codes with a rate-1 inner LDGM code, we also show that these codes achieve the Gilbert-Varshamov bound with asymptotically high probability. This result can be attributed to the presence of the inner rate-1 LDGM code, which is demonstrated to help eliminate high weight codewords in the LDPC code while maintaining a vanishingly small amount of low weight codewords.
A Secure Approach for Caching Contents in Wireless Ad Hoc Networks

Caching aims to store data locally in some nodes within the network to be able to retrieve the contents in shorter time periods. However, caching in the network did not always consider secure storage (due to the compromise between time performance and security). In this paper, a novel decentralized secure coded caching approach is proposed. In this solution, nodes only transmit coded files to avoid eavesdropper wiretappings and protect the user contents. In this technique random vectors are used to combine the contents using XOR operation. We modeled the proposed coded caching scheme by a Shannon cipher system to show that coded caching achieves asymptotic perfect secrecy. The proposed coded caching scheme significantly simplifies the routing protocol in cached networks while it reduces overcaching and achieves a higher throughput capacity compared to uncoded caching in reactive routing. It is shown that with the proposed coded caching scheme any content can be retrieved by selecting a random path while achieving asymptotic optimum solution. We have also studied the cache hit probability and shown that the coded cache hit probability is significantly higher than uncoded caching. A secure caching update algorithm is also presented.
Palindromic subshifts and simple periodic groups of intermediate growth

We describe a new class of groups of Burnside type, giving a procedure transforming an arbitrary non-free minimal action of the dihedral group on a Cantor set into an orbit-equivalent action of an infinite finitely generated periodic group. We show that if the associated Schreier graphs are linearly repetitive, then the group is of intermediate growth. In particular, this gives first examples of simple groups of intermediate growth.
Huge volume expansion and structural transformation of carbon nanotube aligned arrays during electrical breakdown in vacuum

Abstract We observed a huge volume expansion of aligned single walled carbon nanotube (SWCNT) arrays accompanied by structural transformation during electrical breakdown in vacuum. The SWCNT arrays were assembled between prefabricated palladium source and drain electrodes of 2 μm separation on a silicon/silicon dioxide substrate by dielectrophoresis. At high electrical field, the SWCNT arrays erupt into a large mushroom-like structure. Systematic studies with controlled electrical bias show that above a certain field the SWCNTs swell and transform to nanoparticles and flower-like structures with a small volume increase. Further increases in electrical bias and repeated sweeping results in their transformation into amorphous carbon as determined from scanning electron microscopy and transmission electron microscopy (TEM). Cross-sectional studies using a focused ion beam and TEM show the height of a 2–3 nm SWCNT array increased to about 1 μm with a volume increase of ∼400 times. Electron energy loss spectroscopy reveals that graphitic sp2 networks of SWCNT are transformed predominantly to sp3. The current–voltage measurements also show an increase in the resistance of the transformed structure.
Commensurability growths of algebraic groups

Fixing a subgroup $$\varGamma $$ in a group G, the full commensurability growth function assigns to each n the cardinality of the set of subgroups $$\varDelta $$ of G with $$[\varGamma : \varGamma \cap \varDelta ][\varDelta : \varGamma \cap \varDelta ] \le n$$. For pairs $$\varGamma \le G$$, where G is a higher rank Chevalley group scheme defined over $${\mathbb {Z}}$$ and $$\varGamma $$ is an arithmetic lattice in G, we give precise estimates for the full commensurability growth, relating it to subgroup growth and a computable invariant that depends only on G.
Degree Spectra of Relations on a Cone

Let A be a mathematical structure with an additional relation R. We are interested in the degree spectrum of R, either among computable copies of A when (A,R) is a “natural” structure, or (to make this rigorous) among copies of (A,R) computable in a large degree d. We introduce the partial order of degree spectra on a cone and begin the study of these objects. Using a result of Harizanov—that, assuming an effectiveness condition on A and R, if R is not intrinsically computable, then its degree spectrum contains all c.e. degrees—we see that there is a minimal non-trivial degree spectrum on a cone, consisting of the c.e. degrees. We show that this does not generalize to d.c.e. degrees by giving an example of two incomparable degree spectra on a cone. We also give a partial answer to a question of Ash and Knight: they asked whether (subject to some effectiveness conditions) a relation which is not intrinsically ∆α must have a degree spectrum which contains all of the α-CEA degrees. We give a positive answer to this question for α = 2 by showing that any degree spectrum on a cone which strictly contains the ∆2 degrees must contain all of the 2-CEA degrees. We also investigate the particular case of degree spectra on the structure (ω,<). This work represents the beginning of an investigation of the degree spectra of “natural” structures, and we leave many open questions to be answered.
Group Colorings and Bernoulli Subflows

In this paper we study the dynamics of Bernoulli flows and their subflows over general countable groups from the symbolic and topological perspectives. We study free subflows (subflows in which every point has trivial stabilizer), minimal subflows, disjointness of subflows, the problem of classifying subflows up to topological conjugacy, and the differences in dynamical behavior between pairs of points which disagree on finitely many coordinates. We call a point hyper aperiodic if the closure of its orbit is a free subflow and we call it minimal if the closure of its orbit is a minimal subflow. We prove that the set of all (minimal) hyper aperiodic points is always dense but also meager and null. By employing notions and ideas from descriptive set theory, we study the complexity of the sets of hyper aperiodic points and of minimal points and completely determine their descriptive complexity. In doing this we introduce a new notion of countable flecc groups and study their properties. We obtain a dichotomy for the complexity of classifying free subflows up to topological conjugacy. For locally finite groups the topological conjugacy relation for all (free) subflows is hyperfinite and nonsmooth. For nonlocally finite groups the relation is Borel bireducible with the universal countable Borel equivalence relation. A primary focus of the paper is to develop constructive methods for the notions studied. To construct hyper aperiodic points, a fundamental method of construction of multi-layer marker structures is developed with great generality. Variations of the fundamental method are used in many proofs in the paper, and we expect them to be useful more broadly in geometric group theory. As a special case of such marker structures, we study the notion of ccc groups and prove the ccc-ness for countable nilpotent, polycyclic, residually finite, locally finite groups and for free products.
Bowling ball representations of braid groups

In a remark in his seminal 1987 paper, Jones describes a way to define the Burau matrix of a positive braid using a metaphor of bowling a ball down a bowling alley with braided lanes. We extend this definition to allow multiple bowling balls to be bowled simultaneously. We obtain representations of the Iwahori–Hecke algebra and a cabled version of the Temperley–Lieb representation.
The length, width, and inradius of space curves

The width w of a curve $$\gamma $$ in Euclidean space $${\mathbf {R}}^n$$ is the infimum of the distances between all pairs of parallel hyperplanes which bound $$\gamma $$ , while its inradius r is the supremum of the radii of all spheres which are contained in the convex hull of $$\gamma $$ and are disjoint from $$\gamma $$ . We use a mixture of topological and integral geometric techniques, including an application of Borsuk Ulam theorem due to Wienholtz and Crofton’s formulas, to obtain lower bounds on the length of $$\gamma $$ subject to constraints on r and w. The special case of closed curves is also considered in each category. Our estimates confirm some conjectures of Zalgaller up to 99% of their stated value, while we also disprove one of them.
A Mathematical Framework Exhibiting the Emergence of Dynamic Expansion of Task Repertoire in \emph{Pheidole dentata}

The division of labor (DOL) and task allocation (TA) among groups of ants living in a colony is thought to be highly efficient, and key to the robust survival of a colony. A great deal of experimental and theoretical work has been done toward gaining a clear understanding of the evolution of, and underlying mechanisms of these phenomena. Much of this research has utilized mathematical modeling. Here we continue this tradition by developing a mathematical model for a particular aspect of TA, known as age-related repertoire expansion, that has been observed in the minor workers of the ant species Pheidole dentata. In fact, we present a relatively broad mathematical modeling framework based on the dynamics of the frequency with which members of specific age groups carry out distinct tasks. We apply our modeling approach to a specific TA scenario, and compare our theoretical results with experimental data. It is observed that the model predicts perceived behavior, and provides a possible explanation for the aforementioned experimental results.
Hierarchical MPC schemes for periodic systems using stochastic programming

Abstract We show that stochastic programming provides a framework to design hierarchical model predictive control (MPC) schemes for periodic systems. This is based on the observation that, if the state policy of an infinite-horizon problem is periodic, the problem can be cast as a stochastic program (SP). This reveals that it is possible to update periodic state targets by solving a retroactive optimization problem that progressively accumulates historical data. Moreover, we show that the retroactive problem is a statistical approximation of the SP and thus delivers optimal targets in the long run. Notably, the computation of the optimal targets can be achieved without data forecasts. The SP setting also reveals that the retroactive problem can be seen as a high-level hierarchical layer that provides targets to guide a low-level MPC controller that operates over a short period at high time resolution. We derive a retroactive scheme tailored to linear systems by using cutting plane techniques and suggest strategies to handle nonlinear systems and to analyze stability properties.
Cell associations that maximize the average uplink-downlink degrees of freedom

We study the problem of associating mobile terminals to base stations in a linear interference network, with the goal of maximizing the average rate achieved over both the uplink and downlink sessions. The cell association decision is made at a centralized cloud level, with access to global network topology information. More specifically, given the constraint that each mobile terminal can be associated to a maximum of N c base stations at once, we characterize the maximum achievable pre-log factor (degrees of freedom) and the corresponding cell association pattern. Interestingly, the result indicates that for the case where N c ≥ 2, the optimal cell association guarantees the achievability of the maximum uplink rate even when optimizing for the uplink alone, and for the case where N c = 1, the optimal cell association is that of the downlink. Hence, this work draws attention to the question of characterizing network topologies for which the problem can be simplified by optimizing only for the uplink or only for the downlink.
Constrained Submodular Maximization: Beyond 1/e

In this work, we present a new algorithm for maximizing a non-monotone submodular function subject to a general constraint. Our algorithm finds an approximate fractional solution for maximizing the multilinear extension of the function over a down-closed polytope. The approximation guarantee is 0.372 and it is the first improvement over the 1/e approximation achieved by the unified Continuous Greedy algorithm [Feldman et al., FOCS 2011].
Adapting Models to Signal Degradation using Distillation.

Model compression and knowledge distillation have been successfully applied for cross-architecture and cross-domain transfer learning. However, a key requirement is that training examples are in correspondence across the domains. We show that in many scenarios of practical importance such aligned data can be synthetically generated using computer graphics pipelines allowing domain adaptation through distillation. We apply this technique to learn models for recognizing low-resolution images using labeled high-resolution images, non-localized objects using labeled localized objects, line-drawings using labeled color images, etc. Experiments on various fine-grained recognition datasets demonstrate that the technique improves recognition performance on the low-quality data and beats strong baselines for domain adaptation. Finally, we present insights into workings of the technique through visualizations and relating it to existing literature.
Age-optimal Sampling and Transmission Scheduling in Multi-Source Systems

In this paper, we consider the problem of minimizing the age of information in a multi-source system, where samples are taken from multiple sources and sent to a destination via a channel with random delay. Due to interference, only one source can be scheduled at a time. We consider the problem of finding a decision policy that determines the sampling times and transmission order of the sources for minimizing the total average peak age (TaPA) and the total average age (TaA) of the sources. Our investigation of this problem results in an important separation principle: The optimal scheduling strategy and the optimal sampling strategy are independent of each other. In particular, we prove that, for any given sampling strategy, the Maximum Age First (MAF) scheduling strategy provides the best age performance among all scheduling strategies. This transforms our overall optimization problem into an optimal sampling problem, given that the decision policy follows the MAF scheduling strategy. While the zero-wait sampling strategy (in which a sample is generated once the channel becomes idle) is shown to be optimal for minimizing the TaPA, it does not always minimize the TaA. We use Dynamic Programming (DP) to investigate the optimal sampling problem for minimizing the TaA. Finally, we provide an approximate analysis of Bellman's equation to approximate the TaA-optimal sampling strategy by a water-filling solution which is shown to be very close to optimal through numerical evaluations.
Photothermal effects during nanodiamond synthesis from a carbon aerogel in a laser-heated diamond anvil cell

Abstract Nanodiamonds have emerged as promising materials for quantum computing, biolabeling, and sensing due to their ability to host color centers with remarkable photostability and long spin-coherence times at room temperature. Recently, a bottom-up, high-pressure, high-temperature (HPHT) approach was demonstrated for growing nanodiamonds with color centers from amorphous carbon precursors in a laser-heated diamond anvil cell (LH-DAC) that was supported by a near-hydrostatic noble gas pressure medium. However, a detailed understanding of the photothermal heating and its effect on diamond growth, including the phase conversion conditions and the temperature-dependence of color center formation, has not been reported. In this work, we measure blackbody radiation during LH-DAC synthesis of nanodiamond from carbon aerogel to examine these temperature-dependent effects. Blackbody temperature measurements suggest that nanodiamond growth can occur at 16.3 GPa and 1800 K. We use Mie theory and analytical heat transport to develop a predictive photothermal heating model. This model demonstrates that melting the noble gas pressure medium during laser heating decreases the local thermal conductivity to drive a high spatial resolution of phase conversion to diamond. Finally, we observe a temperature-dependent formation of nitrogen vacancy centers and interpret this phenomenon in the context of HPHT carbon vacancy diffusion.
On Fractional GJMS Operators

We describe a new interpretation of the fractional GJMS operators as generalized Dirichlet-to-Neumann operators associated to weighted GJMS operators on naturally associated smooth metric measure spaces. This gives a geometric interpretation of the Caffarelli-Silvestre extension for (−Δ)γ when γ ∊ (0,1), and both a geometric interpretation and a curved analogue of the higher-order extension found by R. Yang for (−Δ)γ when γ > 1. We give three applications of this correspondence. First, we exhibit some energy identities for the fractional GJMS operators in terms of energies in the compactified Poincare-Einstein manifold, including an interpretation as a renormalized energy. Second, for γ ∊ (1,2), we show that if the scalar curvature and the fractional Q-curvature Q2γ of the boundary are nonnegative, then the fractional GJMS operator P2γ is nonnegative. Third, by assuming additionally that Q2γ is not identically zero, we show that P2γ satisfies a strong maximum principle.© 2014 Wiley Periodicals, Inc.
Stringer: Balancing Latency and Resource Usage in Service Function Chain Provisioning

Network function virtualization (NFV) enables telecommunications infrastructure providers to replace special-purpose networking equipment with commodity servers running virtualized network functions (VNFs). A provider using NFV faces the service function chain (SFC) provisioning problem of assigning VNF instances to nodes in the physical infrastructure (for example, datacenters), and routing SFCs (which are sequences of functions required by customers) in the physical network. The provider must balance competing goals of performance and resource usage. This article presents an approach to SFC provisioning, consisting of three elements. The first element is a fast and scalable round-robin heuristic. The second element is a mixed integer programming-based approach. The third element is a queueing-theoretic model to estimate the average latency associated with any SFC provisioning solution. This SFC provisioning system, called Stringer, allows providers to balance the conflicting goals of minimizing infrastructure resources and end-to-end latency for meeting their respective service-level agreements.
ON CYCLES AND COVERINGS ASSOCIATED TO A KNOT

Let be a knot, G be the knot group, K be its commutator subgroup, and x be a distinguished meridian. Let Σ be a finite abelian group. The dynamical system introduced by Silver and Williams in [Augmented group systems and n-knots, Math. Ann.296 (1993) 585–593; Augmented group systems and shifts of finite type, Israel J. Math.95 (1996) 231–251] consisting of the set Hom(K, Σ) of all representations ρ : K → Σ endowed with the weak topology, together with the homeomorphism is finite, i.e. it consists of several cycles. In [Periodic orbits of a dynamical system related to a knot, J. Knot Theory Ramifications20(3) (2011) 411–426] we found the lengths of these cycles for Σ = ℤ/p,p is prime, in terms of the roots of the Alexander polynomial of the knot, mod p. In this paper we generalize this result to a general abelian group Σ. This gives a complete classification of depth 2 solvable coverings over .
Rigid body motion estimation based on the Lagrange-d'Alembert principle

Stable estimation of rigid body pose and velocities from noisy measurements, without any knowledge of the dynamics model, is treated using the Lagrange-d'Alembert principle from variational mechanics. With body-fixed optical and inertial sensor measurements, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in velocity estimation error and the sum of two artificial potential functions; one obtained from a generalization of Wahba's function for attitude estimation and another which is quadratic in the position estimate error. An additional dissipation term that is linear in the velocity estimation error is introduced, and the Lagrange-d'Alembert principle is applied to the Lagrangian with this dissipation. This estimation scheme is discretized using discrete variational mechanics. The presented pose estimator requires optical measurements of at least three inertially fixed landmarks or beacons in order to estimate instantaneous pose. The discrete estimation scheme can also estimate velocities from such optical measurements. In the presence of bounded measurement noise in the vector measurements, numerical simulations show that the estimated states converge to a bounded neighborhood of the actual states.
Cloud Kotta: Enabling secure and scalable data analytics in the cloud

Distributed communities of researchers rely increasingly on valuable, proprietary, or sensitive datasets. Given the growth of such data, especially in fields new to data-driven research like the social sciences and humanities, coupled with what are often strict and complex data-use agreements, many research communities now require methods that allow secure, scalable and cost-effective storage and analysis. Here we present Cloud Kotta: a cloud-based data management and analytics framework. Cloud Kotta delivers an end-to-end solution for coordinating secure access to large datasets, and an execution model that provides both automated infrastructure scaling and support for executing analytics near to the data. Cloud Kotta implements a fine-grained security model ensuring that only authorized users may access, analyze, and download protected data. It also implements automated methods for acquiring and configuring low-cost storage and compute resources as they are needed. We present the architecture and implementation of Cloud Kotta and demonstrate the advantages it provides in terms of increased performance and flexibility. We show that Cloud Kotta's elastic provisioning model can reduce costs by up to 16x when compared with statically provisioned models.
Non-perturbative computation of the strong coupling constant on the lattice

Abstract We review the long term project of the ALPHA collaboration to compute in QCD the running coupling constant and quark masses at high energy scales in terms of low energy hadronic quantities. The adapted techniques required to numerically carry out the required multiscale non-perturbative calculation with our special emphasis on the control of systematic errors are summarized. The complete results in the two dynamical flavor approximation are reviewed and an outlook is given on the ongoing three flavor extension of the programme with improved target precision.
End-to-End Analysis and Design of a Drone Flight Controller

Timing guarantees are crucial to embedded and cyber-physical applications that must bound the end-to-end delay between sensing, processing and actuation. For example, in a flight controller for a multirotor drone, the data from a gyro or inertial sensor must be gathered and processed to determine the attitude of the aircraft. Sensor data fusion is followed by control outputs that alter rotor speeds to adjust the drone’s flight. If the processing pipeline between sensor input and actuation is not bounded, the drone will lose control and possibly fail to maintain flight. This paper describes a composable pipe model for sensor data processing and actuation tasks. The pipe model is used to analyze two end-to-end semantics: 1) freshness and 2) reaction time. We provide a mathematical framework to derive feasible task periods and budgets that satisfy both schedulability and end-to-end timing requirements. We demonstrate the applicability of our design approach by using it to port the Cleanflight flight controller firmware to our in-house real-time operating system called Quest. Experiments show that Cleanflight ported to Quest is able to achieve end-to-end latencies within the predicted time bounds derived by analysis.
Javelin: A Scalable Implementation for Sparse Incomplete LU Factorization

In this work, we present a new scalable incomplete LU factorization framework called Javelin to be used as a preconditioner for solving sparse linear systems with iterative methods. Javelin allows for improved parallel factorization on shared-memory many-core systems by packaging the coefficient matrix into a format that allows for high performance sparse matrix-vector multiplication and sparse triangular solves with minimal overheads. The framework achieves these goals by using a collection of traditional permutations, point-to-point thread synchronizations, tasking, and segmented prefix scans in a conventional compressed sparse row format. Moreover, this framework stresses the importance of co-designing dependent tasks, such as sparse factorization and triangular solves, on highly-threaded architectures. Using these changes, traditional fill-in and drop tolerance methods can be used, while still being able to have observed speedups of up to ~ 42× on 68 Intel Knights Landing cores and ~ 12× on 14 Intel Haswell cores
Vaccine blunts fentanyl potency in male rhesus monkeys.

Abstract One proposed factor contributing to the increased frequency of opioid overdose deaths is the emergence of novel synthetic opioids, including illicit fentanyl and fentanyl analogues. A treatment strategy currently under development to address the ongoing opioid crisis is immunopharmacotherapies or opioid-targeted vaccines. The present study determined the effectiveness and selectivity of a fentanyl-tetanus toxoid conjugate vaccine to alter the behavioral effects of fentanyl and a structurally dissimilar mu-opioid agonist oxycodone in male rhesus monkeys (n = 3–4). Fentanyl and oxycodone produced dose-dependent suppression of behavior in an assay of schedule-controlled responding and antinociception in an assay of thermal nociception (50 °C). Acute naltrexone (0.032 mg/kg) produced an approximate 10-fold potency shift for fentanyl to decrease operant responding. The fentanyl vaccine was administered at weeks 0, 2, 4, 9, 19, and 44 and fentanyl or oxycodone potencies in both behavioral assays were redetermined over the course of 49 weeks. The vaccine significantly and selectively shifted fentanyl potency at least 10-fold in both assays at several time points over the entire experimental period. Mid-point titer levels correlated with fentanyl antinociceptive potency shifts. Antibody affinity for fentanyl as measured by a competitive binding assay improved over time to approximately 3–4 nM. The fentanyl vaccine also increased fentanyl plasma levels approximately 6-fold consistent with the hypothesis that the vaccine sequesters fentanyl in the blood. Overall, these results support the continued development and evaluation of this fentanyl vaccine in humans to address the ongoing opioid crisis.
New developments in revealed preference theory: decisions under risk, uncertainty, and intertemporal choice

This article reviews recent developments in revealed preference theory. It discusses the testable implications of theories of choice that are germane to specific economic environments. The focus is...
A Schematic Model of Baryons and Mesons

If we assume that the strong interactions of baryons and mesons are correctly described in terms of the broken "eightfold way", we are tempted to look for some fundamental explanation of the situation. A highly promised approach is the purely dynamical "bootstrap" model for all the strongly interacting particles within which one may try to derive isotopic spin and strangeness conservation and broken eightfold symmetry from self-consistency alone. Of course, with only strong interactions, the orientation of the asymmetry in the unitary space cannot be specified; one hopes that in some way the selection of specific components of the F-spin by electromagnetism and the weak interactions determines the choice of isotopic spin and hypercharge directions.
The economic impact of network affiliation upon institutions of higher learning

Educational and research institutions are increasingly becoming concerned with determining the best means by which their computing requirements can be satisfied, since these needs are becoming increasingly varied and are continuing to expand. Simply installing more processor power is no longer a solution. More types of software support, ranging from languages to specialized applications packages, are needed. Various types of data bases must be made available. Each of these entails the development of the necessary facilities as well as continuing maintenance. While technological developments are continuing to reduce the cost of raw computing power, the cost of software is continuing to rise relentlessly. Accordingly, it is becoming economically impossible for many institutions to support the breadth and variety of software that their users desire.