cssi/SciSciGPT-SciSciNet huggingface dataset:
Does Tobacco Abstinence Decrease Reward Sensitivity? A Human Laboratory Test.

Introduction Animal studies report abstinence from nicotine makes rewards less rewarding; however, the results of human tests of the effects of cessation on reward sensitivity are mixed. The current study tested reward sensitivity in abstinent smokers using more rigorous methods than most prior studies. Methods A human laboratory study compared outcomes for 1 week prior to quitting to those during 4 weeks postquit. The study used smokers trying to quit, objective and subjective measures, multiple measures during smoking and abstinence, and monetary rewards to increase the prevalence of abstinence. Current daily smokers (n = 211) who were trying to quit completed an operant measure of reward sensitivity and a survey of pleasure from various rewards as well as self-reports of anhedonia, delay discounting, positive affect, and tobacco withdrawal twice each week. A comparison group of long-term former smokers (n = 67) also completed the tasks weekly for 4 weeks. Primary analyses were based on the 61 current smokers who abstained for all 4 weeks. Results Stopping smoking decreased self-reported pleasure from rewards but did not decrease reward sensitivity on the operant task. Abstinence also decreased self-reported reward frequency and increased the two anhedonia measures. However, the changes with abstinence were small for all outcomes (6%-14%) and most lasted less than a week. Conclusions Abstinence from tobacco decreased most self-report measures of reward sensitivity; however, it did not change the objective measure. The self-report effects were small. Implications Animal research suggests that nicotine withdrawal decreases reward sensitivity. Replication tests of this in humans have produced inconsistent results. We report what we believe is a more rigorous test. We found smoking abstinence slightly decreases self-reports of reward sensitivity but does not do so for a behavioral measure of reward sensitivity.
Protein structure-based design, synthesis, and biological evaluation of 5-thia-2,6-diamino-4(3H)-oxopyrimidines: potent inhibitors of glycinamide ribonucleotide transformylase with potent cell growth inhibition

The design, synthesis, biochemical, and biological evaluation of a novel series of 5-thia-2,6-diamino-4(3H)-oxopyrimidine inhibitors of glycinamide ribonucleotide transformylase (GART) are described. The compounds were designed using the X-ray crystal structure of human GART. The monocyclic 5-thiapyrimidinones were synthesized by coupling an alkyl thiol with 5-bromo-2, 6-diamino-4(3H)-pyrimidinone, 20. The bicyclic compounds were prepared in both racemic and diastereomerically pure forms using two distinct synthetic routes. The compounds were found to have human GART KiS ranging from 30 microM to 2 nM. The compounds inhibited the growth of both L1210 and CCRF-CEM cells in culture with potencies down to the low nanomolar range and were found to be selective for the de novo purine biosynthesis pathway. The most potent inhibitors had 2,5-disubstituted thiophene rings attached to the glutamate moiety. Placement of a methyl substituent at the 4-position of the thiophene ring to give compounds 10, 18, and 19 resulted in inhibitors with significantly decreased mFBP affinity.
Disrupting narrow conceptions of justice: Exploring and expanding ‘bullying’ and ‘upstanding’ in a university honors course:

The term ‘upstanding’ encompasses actions and behaviors grounded in one’s defense of their own beliefs and others. While such broad application of the term has merit, from a critical education pers...
Microbial metabolite deoxycholic acid shapes microbiota against Campylobacter jejuni chicken colonization

Despite reducing the prevalent foodborne pathogen Campylobacter jejuni in chickens decreases campylobacteriosis, few effective approaches are available. The aim of this study was to use microbial metabolic product bile acids to reduce C. jejuni chicken colonization. Broiler chicks were fed with deoxycholic acid (DCA), lithocholic acid (LCA), or ursodeoxycholic acid (UDCA). The birds were also transplanted with DCA modulated anaerobes (DCA-Anaero) or aerobes (DCA-Aero). The birds were infected with human clinical isolate C. jejuni 81-176 or chicken isolate C. jejuni AR101. Notably, C. jejuni 81-176 was readily colonized intestinal tract at d16 and reached an almost plateau at d21. Remarkably, DCA excluded C. jejuni cecal colonization below the limit of detection at 16 and 28 days of age. Neither chicken ages of infection nor LCA or UDCA altered C. jejuni AR101 chicken colonization level, while DCA reduced 91% of the bacterium in chickens at d28. Notably, DCA diet reduced phylum Firmicutes but increased Bacteroidetes compared to infected control birds. Importantly, DCA-Anaero attenuated 93% of C. jejuni colonization at d28 compared to control infected birds. In conclusion, DCA shapes microbiota composition against C. jejuni colonization in chickens, suggesting a bidirectional interaction between microbiota and microbial metabolites.
High-Dimensional Uncertainty Quantification of Electronic and Photonic IC With Non-Gaussian Correlated Process Variations

Uncertainty quantification based on generalized polynomial chaos has been used in many applications. It has also achieved great success in variation-aware design automation. However, almost all existing techniques assume that the parameters are mutually independent or Gaussian correlated, which is rarely true in real applications. For instance, in chip manufacturing, many process variations are actually correlated. Recently, some techniques have been developed to handle non-Gaussian correlated random parameters, but they are time-consuming for high-dimensional problems. We present a new framework to solve uncertainty quantification problems with many non-Gaussian correlated uncertainties. First, we propose a set of smooth basis functions to well capture the impact of non-Gaussian correlated process variations. We develop a tensor approach to compute these basis functions in a high-dimension setting. Second, we investigate the theoretical aspect and practical implementation of a sparse solver to compute the coefficients of all basis functions. We provide some theoretical analysis for the exact recovery condition and error bound of this sparse solver in the context of uncertainty quantification. We present three adaptive sampling approaches to improve the performance of the sparse solver. Finally, we validate our methods by synthetic and practical electronic/photonic ICs with 19 to 57 non-Gaussian correlated variation parameters. Our approach outperforms Monte Carlo by thousands of times in terms of efficiency. It can also accurately predict the output density functions with multiple peaks caused by non-Gaussian correlations, which are hard to capture by existing methods.
On the free Lie algebra with multiple brackets

It is a classical result that the multilinear component of the free Lie algebra is isomorphic (as a representation of the symmetric group) to the top (co)homology of the proper part of the poset of partitions ź n tensored with the sign representation. We generalize this result in order to study the multilinear component of the free Lie algebra with multiple compatible Lie brackets. We introduce a new poset of weighted partitions ź n k that allows us to generalize the result. The new poset is a generalization of ź n and of the poset of weighted partitions ź n w introduced by Dotsenko and Khoroshkin and studied by the author and Wachs for the case of two compatible brackets. We prove that the poset ź n k with a top element added is EL-shellable and hence Cohen-Macaulay. This and other properties of ź n k enable us to answer questions posed by Liu on free multibracketed Lie algebras. In particular, we obtain various dimension formulas and multicolored generalizations of the classical Lyndon and comb bases for the multilinear component of the free Lie algebra. We also obtain a plethystic formula for the Frobenius characteristic of the representation of the symmetric group on the multilinear component of the free multibracketed Lie algebra.
Changepoint Detection over Graphs with the Spectral Scan Statistic

We consider the change-point detection problem of deciding, based on noisy measurements, whether an unknown signal over a given graph is constant or is instead piecewise constant over two induced subgraphs of relatively low cut size. We analyze the corresponding generalized likelihood ratio (GLR) statistic and relate it to the problem of nding a sparsest cut in a graph. We develop a tractable relaxation of the GLR statistic based on the combinatorial Laplacian of the graph, which we call the spectral scan statistic, and analyze its properties. We show how its performance as a testing procedure depends directly on the spectrum of the graph, and use this result to explicitly derive its asymptotic properties on few graph topologies. Finally, we demonstrate both theoretically and by simulations that the spectral scan statistic can outperform naive testing procedures based on edge thresholding and 2 testing.
Evaluating power system vulnerability to false data injection attacks via scalable optimization

Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack is chosen to maximize the physical power flow on a target line subsequent to re-dispatch. This problem can be solved as a mixed-integer linear program, but it is difficult to scale to large systems due to numerical challenges. Three new computationally efficient algorithms to solve this problem are presented. These algorithms provide lower and upper bounds on the system vulnerability measured as the maximum power flow subsequent to an attack. Using these techniques, vulnerability assessments are conducted for IEEE 118-bus system and Polish system with 2383 buses.
Energy and asymptotics of Ricci-flat 4-manifolds with a Killing field

Given a complete, Ricci-flat 4-manifold with a Killing field, we give an estimate on the manifold's energy in terms of a certain asymptotic quantity of the Killing field. If the Killing field has no zeros and satisfies a certain asymptotic condition, the manifold is flat.
Cardinal inequalities for S(n) -spaces

Hajnal and Juhasz [9] proved that if X is a T1-space, then $${|X| \leq 2^{s(X)\psi(X)}}$$ , and if X is a Hausdorff space, then $${|X| \leq 2^{c(X)\chi(X)}}$$ and $${|X| \leq 2^{2^{s(X)}}}$$ . Schroder sharpened the first two estimations by showing that if X is a Hausdorff space, then $${|X| \leq 2^{Us(X)\psi_c(X)}}$$ , and if X is a Urysohn space, then $${|X| \leq 2^{Uc(X)\chi(X)}}$$ . In this paper, for any positive integer n and some topological spaces X, we define the cardinal functions $${\chi_n(X), \psi_n(X), s_n(X)}$$ , and cn(X) called respectively S(n)-character, S(n)-pseudocharacter, S(n)-spread, and S(n)-cellularity and using these new cardinal functions we show that the above-mentioned inequalities could be extended to the class of S(n)-spaces. We recall that the S(1)-spaces are exactly the Hausdorff spaces and the S(2)-spaces are exactly the Urysohn spaces.
BOUNDS ON THE CROSSCAP NUMBER OF TORUS KNOTS

For a torus knot K, we bound the crosscap number c(K) in terms of the genus g(K) and crossing number n(K) : c(K) ≤ ⌊(g(K)+9)/6⌋ and c(K) ≤ ⌊(n(K) + 16)/12⌋. The (6n - 2,3) torus knots show that these bounds are sharp.
Key Exchange Trust Evaluation in Peer-to-Peer Sensor Networks With Unconditionally Secure Key Exchange

As the utilization of sensor networks continue to increase, the importance of security becomes more profound. Many industries depend on sensor networks for critical tasks, and a malicious entity can potentially cause catastrophic damage. We propose a new key exchange trust evaluation for peer-to-peer sensor networks, where part of the network has unconditionally secure key exchange. For a given sensor, the higher the portion of channels with unconditionally secure key exchange the higher the trust value. We give a brief introduction to unconditionally secured key exchange concepts and mention current trust measures in sensor networks. We demonstrate the new key exchange trust measure on a hypothetical sensor network using both wired and wireless communication channels.
Fast nonparametric conditional density estimation

Conditional density estimation generalizes regression by modeling a full density f(y|x) rather than only the expected value E(y|x). This is important for many tasks, including handling multi-modality and generating prediction intervals. Though fundamental and widely applicable, nonparametric conditional density estimators have received relatively little attention from statisticians and little or none from the machine learning community. None of that work has been applied to greater than bivariate data, presumably due to the computational difficulty of data-driven bandwidth selection. We describe the double kernel conditional density estimator and derive fast dual-tree-based algorithms for bandwidth selection using a maximum likelihood criterion. These techniques give speedups of up to 3.8 million in our experiments, and enable the first applications to previously intractable large multivariate datasets, including a redshift prediction problem from the Sloan Digital Sky Survey.
Vertex-colored graphs, bicycle spaces and Mahler measure

The space &#x1d49e; of conservative vertex colorings (over a field &#x1d53d;) of a countable, locally finite graph G is introduced. When G is connected, the subspace &#x1d49e;0 of based colorings is shown to be isomorphic to the bicycle space of the graph. For graphs G with a cofinite free ℤd-action by automorphisms, &#x1d49e; is dual to a finitely generated module over the polynomial ring &#x1d53d;[x1±1,…,x d±1]. Polynomial invariants for this module, the Laplacian polynomials Δk,k ≥ 0, are defined, and their properties are discussed. The logarithmic Mahler measure of Δ0 is characterized in terms of the growth of spanning trees.
Intrinsic stratifications of analytic varieties

By associating a Lie algebra of analytic vector elds to every point of an analytic variety and using the associated Nagano foliation, this work presents a coordinate-free stratication of analytic sets.
On strictly nonzero integer-valued charges

A charge (finitely additive measure) defined on a Boolean algebra of sets taking values in a group $G$ is called a strictly nonzero (SNZ) charge if it takes the identity value in $G$ only for the zero element of the Boolean algebra. A study of such charges was initiated by Rudiger Gobel and K.P.S. Bhaskara Rao in 2002. Our main result is a solution to one of the questions posed in that paper: we show that for every cardinal $\aleph$, the Boolean algebra of clopen sets of $\{0,1\}^\aleph$ has a strictly nonzero integer-valued charge. The key lemma that we prove is that there exists a strictly nonzero integer-valued permutation-invariant charge on the Boolean algebra of clopen sets of $\{0,1\}^{\aleph_0}$. Our proof is based on linear-algebraic arguments, as well as certain kinds of polynomial approximations of binomial coefficients. We also show that there is no integer-valued SNZ charge on ${\mathcal{P}}(N)$. Finally, we raise some interesting problems on integer-valued SNZ charges.
Correlations of electrons from heavy flavor decay in p+p, d+Au and Au+Au collisions

In relativistic heavy ion collisions heavy flavor probes are crucial to understand the interactions between partons and the produced hot nuclear matter. Measurements in p+p collisions provide information about how the heavy quarks are produced and fragment and in d+Au collisions are sensitive to possible effects from cold nuclear matter. Azimuthal correlation measurements involving heavy flavor probes are complementary to single particle spectra measurements and provide additional information about production and interactions of heavy quarks. Measurements of electrons with heavy flavor decay with other hadrons from the event can provide information about how the heavy quark interacts with the produced matter and can be compared to similar measurements from light hadron correlations. Correlations between electrons from heavy flavor decay with muons, also from heavy flavor decay, can provide further information about heavy flavor production and cold nuclear matter effects in d+Au collisions with a very clean signal. We present PHENIX results for electron-hadron correlations in p+p and Au+Au collisions and electron-muon correlations in p+p and d+Au collisions and discuss the implications of these measurements.
Plasticity and Dislocation Dynamics in a Phase Field Crystal Model

The critical dynamics of dislocation avalanches in plastic flow is examined using a phase field crystal model. In the model, dislocations are naturally created, without any ad hoc creation rules, by applying a shearing force to the perfectly periodic ground state. These dislocations diffuse, interact and annihilate with one another, forming avalanche events. By data collapsing the event energy probability density function for different shearing rates, a connection to interface depinning dynamics is confirmed. The relevant critical exponents agree with mean field theory predictions.
Well-posedness and continuity properties of the Fornberg–Whitham equation in Besov spaces

Abstract In this paper, we prove well-posedness of the Fornberg–Whitham equation in Besov spaces B 2 , r s in both the periodic and non-periodic cases. This will imply the existence and uniqueness of solutions in the aforementioned spaces along with the continuity of the data-to-solution map provided that the initial data belongs to B 2 , r s . We also establish sharpness of continuity on the data-to-solution map by showing that it is not uniformly continuous from any bounded subset of B 2 , r s to C ( [ − T , T ] ; B 2 , r s ) . Furthermore, we prove a Cauchy–Kowalevski type theorem for this equation that establishes the existence and uniqueness of real analytic solutions and also provide blow-up criterion for solutions.
Accessing Generalized Transversity Distributions with Exclusive $\pi^o$ Electroproduction

Exclusive $\pi^0$ electroproduction from nucleons at large $Q^2$ can be described by Generalized Parton Distributions (GPDs), particularly the chiral odd subset related to transversity. These GPDs can be accessed experimentally from various cross sections and asymmetries. We calculate these GPDs in a spectator model, constrained by boundary functions. Alternatively, in a hadronic picture the meson production amplitudes correspond to C-odd Regge exchanges with final state interactions. The helicity structure provides relations between the partonic and the hadronic, Regge description of C-odd, chiral-odd processes. Calculations show how the tensor charge and other transversity parameters can be
XOR-Sampling for Network Design with Correlated Stochastic Events

Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent. In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated. We use Markov Random Fields to model the correlation and define a new stochastic network design framework. We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler. The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.
Classification of screening systems for lattice vertex operator algebras

We study and classify systems of certain screening operators arising in a generalized vertex operator algebra, or more generally an abelian intertwining algebra with an associated vertex operator (super)algebra. Screening pairs arising from weight one primary vectors acting commutatively on a lattice vertex operator algebra (the vacuum module) are classified into four general types; one type of which has been shown to play an important role in the construction and study of certain important families of $${\mathcal {W}}$$ -vertex algebras. These types of screening pairs which we go on to study in detail through the notion of a system of screeners are lattice elements or “screening momenta” which give rise to screening pairs. We classify screening systems for all positive definite integral lattices of rank two, and for all positive definite even lattices of arbitrary rank when these lattices are generated by a screening system.
Universality of the Stochastic Bessel Operator

We establish universality at the hard edge for general beta ensembles assuming that: the background potential V is a polynomial such that $$x \mapsto V(x^2)$$ is strongly convex, $$\beta \ge 1$$ , and the “dimension-difference” parameter $$a\ge 0$$ . The method rests on the corresponding tridiagonal matrix models, showing that their appropriate continuum scaling limit is given by the Stochastic Bessel Operator. As conjectured in Edelman and Sutton (J Stat Phys 127:1121–1165, 2007) and rigorously established in Ramirez and Rider (Commun Math Phys 288:887–906, 2009), the latter characterizes the hard edge in the case of linear potential and all $$\beta $$ (the classical “beta-Laguerre” ensembles).
Encapsulated Silicon Nitride Nanobeam Cavity for Nanophotonics using Layered Materials

The fragile nature of floating membrane resonators poses a serious problem for constructing a hybrid photonics platform. To overcome this challenge, we design and demonstrate encapsulated silicon nitride nanobeams and demonstrate coupling to layered materials.
Simulation Study on Collaborative Content Distribution in Delay Tolerant Vehicular Networks

Modern vehicles are equipped with more and more sophisticated computer modules, which need to periodically download files from the cloud, such as security certificates, digital maps, system firmwares, etc. Collaborative content distribution utilizes V2V communication to distribute large files across the vehicular networks. It has the potential to significantly reduce the cost of cellular-based communication such as 4G LTE. In this report, we have conducted a simulation study to verify the feasibility of a hybrid cellular and V2V collaborative content distribution network. In our simulation, a small portion of the simulated vehicles download the file directly from the cloud via cellular communication, while other vehicles receive the file via collaborative V2V communications. Our simulation results show that, with only 1% of vehicles enabled with cellular communication, it takes less than 24 hours to distribute a file to 90% of the vehicles in a metropolitan area, and around 48 to 72 hours to distribute to 99%. The results are very promising for many delay-tolerant content distribution applications in vehicular networks.
Persistence of centrality in random growing trees

We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each node, with the most central node being the tree centroid. For random trees grown according to a preferential attachment model, a uniform attachment model, or a diffusion processes over a regular tree, we prove that a single node persists as the tree centroid after a finite number of steps, with probability 1. Furthermore, this persistence property generalizes to the top K ≥ 1 nodes with respect to the same centrality measure. We also establish necessary and sufficient conditions for the size of an initial seed graph required to ensure persistence of a particular node with probability 1−ϵ, as a function of ϵ: In the case of preferential and uniform attachment models, we derive bounds for the size of an initial hub constructed around the special node. In the case of a diffusion process over a regular tree, we derive bounds for the radius of an initial ball centered around the special node. Our necessary and sufficient conditions match up to constant factors for preferential attachment and diffusion tree models.
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs.

We study multi-armed bandit problems with graph feedback, in which the decision maker is allowed to observe the neighboring actions of the chosen action, in a setting where the graph may vary over time and is never fully revealed to the decision maker. We show that when the feedback graphs are undirected, the original Thompson Sampling achieves the optimal (within logarithmic factors) regret $\tilde{O}\left(\sqrt{\beta_0(G)T}\right)$ over time horizon $T$, where $\beta_0(G)$ is the average independence number of the latent graphs. To the best of our knowledge, this is the first result showing that the original Thompson Sampling is optimal for graphical bandits in the undirected setting. A slightly weaker regret bound of Thompson Sampling in the directed setting is also presented. To fill this gap, we propose a variant of Thompson Sampling, that attains the optimal regret in the directed setting within a logarithmic factor. Both algorithms can be implemented efficiently and do not require the knowledge of the feedback graphs at any time.
Towards visual ego-motion learning in robots

Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.
Causal conclusions that flip repeatedly and their justification

Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery cannot be reliable on a given sample size. We argue that since repeated flipping of causal conclusions is unavoidable in principle for consistent methods, the best possible discovery methods are consistent methods that retract their earlier conclusions no more than necessary. A series of simulations of various methods across a wide range of sample sizes illustrates concretely both the theorem and the principle of comparing methods in terms of retractions.
Diffractive optics approach towards subwavelength pixels

Pixel size in cameras and other refractive imaging devices is typically limited by the free-space diffraction. However, a vast majority of semiconductor-based detectors are based on materials with substantially high refractive index. We demonstrate that diffractive optics can be used to take advantage of this high refractive index to reduce effective pixel size of the sensors below free-space diffraction limit. At the same time, diffractive systems encode both amplitude and phase information about the incoming beam into multiple pixels, offering the platform for noise-tolerant imaging with dynamical refocusing. We explore the opportunities opened by high index diffractive optics to reduce sensor size and increase signalto- noise ratio of imaging structures.
A Geometric Perspective on Sparse Filtrations

We present a geometric perspective on sparse ltrations used in topological data analysis. This new perspective leads to much simpler proofs, while also being more general, applying equally to Rips ltrations and Cech ltrations for any convex metric. We also give an algorithm for nding the simplices in such a ltration and prove that the vertex removal can be implemented as a sequence of elementary edge collapses. A video illustrating this approach is available [7].
Null surgery on knots in L-spaces

Let K be a knot in an L-space Y with a Dehn surgery to a surface bundle over S^1. We prove that K is rationally fibered, that is, the knot complement admits a fibration over S^1. As part of the proof, we show that if K C Y has a Dehn surgery to S^1 x S^2, then K is rationally fibered. In the case that K admits some S^1 x S^2 surgery, K is Floer simple, that is, the rank of HFK(Y,K) is equal to the order of H_1(Y). By combining the latter two facts, we deduce that the induced contact structure on the ambient manifold Y is tight. In a different direction, we show that if K is a knot in an L-space Y, then any Thurston norm minimizing rational Seifert surface for K extends to a Thurston norm minimizing surface in the manifold obtained by the null surgery on K (i.e., the unique surgery on K with b_1 > 0).
Convexity of Mutual Information Along the Heat Flow

We study the convexity of mutual information along the evolution of the heat equation. We prove that if the initial distribution is log-concave, then mutual information is always a convex function of time. We also prove that if the initial distribution is either bounded, or has finite fourth moment and Fisher information, then mutual information is eventually convex, i.e., convex for all large time. Finally, we provide counterexamples to show that mutual information can be nonconvex at small time.
The quantum drift-diffusion model: Existence and exponential convergence to the equilibrium

Abstract This work is devoted to the analysis of the quantum drift-diffusion model derived by Degond et al. in [7] . The model is obtained as the diffusive limit of the quantum Liouville–BGK equation, where the collision term is defined after a local quantum statistical equilibrium. The corner stone of the model is the closure relation between the density and the current, which is nonlinear and nonlocal, and is the main source of the mathematical difficulties. The question of the existence of solutions has been open since the derivation of the model, and we provide here a first result in a one-dimensional periodic setting. The proof is based on an approximation argument, and exploits some properties of the minimizers of an appropriate quantum free energy. We investigate as well the long time behavior, and show that the solutions converge exponentially fast to the equilibrium. This is done by deriving a non-commutative logarithmic Sobolev inequality for the local quantum statistical equilibrium.
Approximate correctors and convergence rates in almost-periodic homogenization

Abstract We carry out a comprehensive study of quantitative homogenization of second-order elliptic systems with bounded measurable coefficients that are almost-periodic in the sense of H. Weyl. We obtain uniform local L 2 estimates for approximate correctors in terms of a function that quantifies the almost-periodicity of the coefficient matrix. We give a condition that implies the existence of (true) correctors. These estimates as well as similar estimates for the dual approximate correctors yield optimal or near optimal convergence rates in H 1 and L 2 . The L 2 -based Holder and Lipschitz estimates at large scale are also established.
Simple Algorithms for Optimization on Riemannian Manifolds with Constraints

We consider optimization problems on manifolds with equality and inequality constraints. A large body of work treats constrained optimization in Euclidean spaces. In this work, we consider extensions of existing algorithms from the Euclidean case to the Riemannian case. Thus, the variable lives on a known smooth manifold and is further constrained. In doing so, we exploit the growing literature on unconstrained Riemannian optimization. For the special case where the manifold is itself described by equality constraints, one could in principle treat the whole problem as a constrained problem in a Euclidean space. The main hypothesis we test here is whether it is sometimes better to exploit the geometry of the constraints, even if only for a subset of them. Specifically, this paper extends an augmented Lagrangian method and smoothed versions of an exact penalty method to the Riemannian case, together with some fundamental convergence results. Numerical experiments indicate some gains in computational efficiency and accuracy in some regimes for minimum balanced cut, non-negative PCA and k-means, especially in high dimensions.
Affine Hermitian-Einstein Metrics

Here c1(E, h) is the first Chern form of E with respect to a Hermitian metric h. The famous theorem of Donaldson [7, 8] (for algebraic manifolds only) and Uhlenbeck-Yau [24, 25] says that an irreducible vector bundle E → N is ω-stable if and only if it admits a HermitianEinstein metric (i.e. a metric whose curvature, when the 2-form part is contracted with the metric on N , is a constant times the identity endomorphism on E). This correspondence between stable bundles and Hermitian-Einstein metrics is often called the Kobayashi-Hitchin correspondence. An important generalization of this theorem is provided by Li-Yau [15] for complex manifolds (and subsequently due to Buchdahl by a different method for surfaces [3]). The major insight for this extension is the fact that the degree is well-defined as long as the Hermitian form ω on N satisfies only ∂∂ωn−1 = 0. This is because
ORIENTABILITY AND REAL SEIBERG-WITTEN INVARIANTS

We investigate the Seiberg–Witten theory in the presence of real structures. Certain conditions are obtained so that integer-valued real Seiberg–Witten invariants can be defined. In general, we study properties of the real Seiberg–Witten projection map from the point of view of Fredholm map degrees.
Distance structures for generalized metric spaces

Abstract Let R = ( R , ⊕ , ≤ , 0 ) be an algebraic structure, where ⊕ is a commutative binary operation with identity 0, and ≤ is a translation-invariant total order with least element 0. Given a distinguished subset S ⊆ R , we define the natural notion of a “generalized” R -metric space, with distances in S. We study such metric spaces as first-order structures in a relational language consisting of a distance inequality for each element of S. We first construct an ordered additive structure S ⁎ on the space of quantifier-free 2-types consistent with the axioms of R -metric spaces with distances in S, and show that, if A is an R -metric space with distances in S, then any model of Th ( A ) logically inherits a canonical S ⁎ -metric. Our primary application of this framework concerns countable, universal, and homogeneous metric spaces, obtained as generalizations of the rational Urysohn space. We adapt previous work of Delhomme, Laflamme, Pouzet, and Sauer to fully characterize the existence of such spaces. We then fix a countable totally ordered commutative monoid R , with least element 0, and consider U R , the countable Urysohn space over R . We show that quantifier elimination for Th ( U R ) is characterized by continuity of addition in R ⁎ , which can be expressed as a first-order sentence of R in the language of ordered monoids. Finally, we analyze an example of Casanovas and Wagner in this context.
Statistical analysis of synchrosqueezed transforms

Abstract Synchrosqueezed transforms are non-linear processes for a sharpened time–frequency representation of wave-like components. They are efficient tools for identifying and analyzing wave-like components from their superposition. This paper is concerned with the statistical properties of compactly supported synchrosqueezed transforms for wave-like components embedded in a generalized Gaussian random process in multidimensional spaces. Guided by the theoretical analysis of these properties, new numerical implementations are proposed to reduce the noise fluctuations of these transforms on noisy data. A MATLAB package SynLab together with several heavily noisy examples is provided to support these theoretical claims.
Model-based reinforcement learning with parametrized physical models and optimism-driven exploration

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm.
Adaptive Object Detection Using Adjacency and Zoom Prediction

State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
Diameter-Based Active Learning

To date, the tightest upper and lower-bounds for the active learning of general concept classes have been in terms of a parameter of the learning problem called the splitting index. We provide, for the first time, an efficient algorithm that is able to realize this upper bound, and we empirically demonstrate its good performance.
Multi-Scale Context Aggregation by Dilated Convolutions

Abstract: State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.
Revisiting Character-Based Neural Machine Translation with Capacity and Compression

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering. However, it results in longer sequences in which each symbol contains less information, creating both modeling and computational challenges. In this paper, we show that the modeling problem can be solved by standard sequence-to-sequence architectures of sufficient depth, and that deep models operating at the character level outperform identical models operating over word fragments. This result implies that alternative architectures for handling character input are better viewed as methods for reducing computation time than as improved ways of modeling longer sequences. From this perspective, we evaluate several techniques for character-level NMT, verify that they do not match the performance of our deep character baseline model, and evaluate the performance versus computation time tradeoffs they offer. Within this framework, we also perform the first evaluation for NMT of conditional computation over time, in which the model learns which timesteps can be skipped, rather than having them be dictated by a fixed schedule specified before training begins.
A Mixture Model for Aggregation of Multiple Pre-Trained Weak Classifiers

Deep networks have gained immense popularity in Computer Vision and other fields in the past few years due to their remarkable performance on recognition/classification tasks surpassing the state-of-the art. One of the keys to their success lies in the richness of the automatically learned features. In order to get very good accuracy, one popular option is to increase the depth of the network. Training such a deep network is however infeasible or impractical with moderate computational resources and budget. The other alternative to increase the performance is to learn multiple weak classifiers and boost their performance using a boosting algorithm or a variant thereof. But, one of the problems with boosting algorithms is that they require a re-training of the networks based on the misclassified samples. Motivated by these problems, in this work we propose an aggregation technique which combines the output of multiple weak classifiers. We formulate the aggregation problem using a mixture model fitted to the trained classifier outputs. Our model does not require any re-training of the "weak" networks and is computationally very fast (takes < 30 seconds to run in our experiments). Thus, using a less expensive training stage and without doing any re-training of networks, we experimentally demonstrate that it is possible to boost the performance by 12%. Furthermore, we present experiments using hand-crafted features and improved the classification performance using the proposed aggregation technique. One of the major advantages of our framework is that our framework allows one to combine features that are very likely to be of distinct dimensions since they are extracted using different networks/algorithms. Our experimental results demonstrate a significant performance gain from the use of our aggregation technique at a very small computational cost.
A Game-Theoretic Framework for the Virtual Machines Migration Timing Problem

In a multi-tenant cloud, a number of Virtual Machines (VMs) are collocated on the same physical machine to optimize performance, power consumption and maximize profit. This, however, increases the risk of a malicious VM performing side-channel attacks and leaking sensitive information from neighboring VMs. As such, this paper develops and analyzes a game-theoretic framework for the VM migration timing problem in which the cloud provider decides when to migrate a VM to a different physical machine to reduce the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker’s payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings as well as a proof-of-concept implementation in a realistic cloud setting.
Electrochemical Impedance of a Battery Electrode with Anisotropic Active Particles

Abstract Electrochemical impedance spectra for battery electrodes are usually interpreted using models that assume isotropic active particles, having uniform current density and symmetric diffusivities. While this can be reasonable for amorphous or polycrystalline materials with randomly oriented grains, modern electrode materials increasingly consist of highly anisotropic, single-crystalline, nanoparticles, with different impedance characteristics. In this paper, analytical expressions are derived for the impedance of anisotropic particles with tensorial diffusivities and orientation-dependent surface reaction rates and capacitances. The resulting impedance spectrum contains clear signatures of the anisotropic material properties and aspect ratio, as well as statistical variations in any of these parameters.
Oscillation energy based sensitivity analysis and control for multi-mode oscillation systems

This paper describes a novel approach to analyze and control systems with multi-mode oscillation problems. Traditional single dominant mode analysis fails to provide effective control actions when several modes have similar low damping ratios. This work addresses this problem by considering all modes in the formulation of the system kinetic oscillation energy. The integral of energy over time defines the total action as a measure of dynamic performance, and its sensitivity allows comparing the performance of different actuators/locations in the system to select the most effective one to damp the oscillation energy. Time domain simulations in the IEEE 9-bus system and IEEE 39-bus system verify the findings obtained by the oscillation energy based analysis. Applications of the proposed method in control and system planning are discussed.