cssi/SciSciGPT-SciSciNet huggingface dataset:
Functional connectivity networks with and without global signal correction

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.
The synthesis of difluoro[1]benzothieno[2,3-c]quinolines and their N-methyl quaternary salts

A series of difluoro[1]benzothieno[2,3-c]quinolines has been prepared by photocyclization of the appropriate carboxamides. The lactams obtained were converted into the corresponding chloro derivatives which were catalytically dechlorinated into the difluoro[1] benzothieno[2,3-c]quinolines. The latter compounds were transformed into the N-methyl quaternary salts.
Magnetic Ordering and Spin-Glass Behavior in First-Row Transition Metal Hexacyanomanganate(IV) Prussian Blue Analogues

Magnetically ordered Prussian blue analogues with the general formulation of M[Mn(CN)6] (M = V, Cr, Mn, Co, Ni) were made in aprotic media utilizing [MnIV(CN)6]2-. These analogs are valence-ambiguo...
Cryptochromes-Mediated Inhibition of the CRL4Cop1-Complex Assembly Defines an Evolutionary Conserved Signaling Mechanism.

Summary In plants, cryptochromes are photoreceptors that negatively regulate the ubiquitin ligase CRL4Cop1. In mammals, cryptochromes are core components of the circadian clock and repressors of the glucocorticoid receptor (GR). Moreover, mammalian cryptochromes lost their ability to interact with Cop1, suggesting that they are unable to inhibit CRL4Cop1. Contrary to this assumption, we found that mammalian cryptochromes are also negative regulators of CRL4Cop1, and through this mechanism, they repress the GR transcriptional network both in cultured cells and in the mouse liver. Mechanistically, cryptochromes inactivate Cop1 by interacting with Det1, a subunit of the mammalian CRL4Cop1 complex that is not present in other CRL4s. Through this interaction, the ability of Cop1 to join the CRL4 complex is inhibited; therefore, its substrates accumulate. Thus, the interaction between cryptochromes and Det1 in mammals mirrors the interaction between cryptochromes and Cop1 in planta, pointing to a common ancestor in which the cryptochromes-Cop1 axis originated.
Nitromethane with IBX/TBAF as a nitrosating agent: synthesis of nitrosamines from secondary or tertiary amines under mild conditions.

Aliphatic or aromatic N,N-disubstituted nitrosamine was generated in fair to excellent yield from the reaction of a secondary or tertiary amine with o-iodoxybenzoic acid (IBX) or o-iodosylbenzoic acid (IBA)/R4NX (X = halide) and nitromethane. The product yield was strongly influenced by both the halide of R4NX and iodanes. IBX gave a higher yield than IBA, while the halides follow F– > Cl– > Br– ∼ I–. Nitrous acid formed in situ from nitromethane and IBX (or IBA)/halides is likely responsible for the observed reaction.
New Efficient Approach to 2,2-Diaryl-1,1-difluoro-1-alkenes and 1,1-Difluoro-2-aryl-1,3-dienes via Suzuki Coupling of α-Halo-β,β-difluorostyrenes

α-Halo-β,β-difluorostyrenes [ArCX = CF2; X = Br, I; Ar = aryl, heteroaryl; synthesized by the Pd(0)-catalyzed coupling reaction of the corresponding α-halo-β,β-difluoroethenylzinc reagents (CF2CXZnCl, X = Br, I) with aryl iodides] were functionalized at the halogen site with arylboronic acids under Pd(0)-catalyzed Suzuki−Miyaura coupling reaction conditions to obtain 2,2-diaryl-1,1-difluoro-1-alkenes (ArAr‘CCF2, Ar‘ = aryl, heteroaryl) in 51−91% isolated yield. The corresponding reaction with alkenylboronic acids produced 1,1-difluoro-2-aryl-1,3-dienes in 53−80% isolated yield. Alternatively, 2,2-disubstituted-1,1-difluoro-1-alkenes were synthesized in moderate yield by a zinc-insertion reaction at the halogen site of the α-halo-β,β-difluorostyrenes, followed by Pd(0)-catalyzed cross-coupling of the zinc reagent with aryl or alkenyl iodides.
Corals sustain growth but not skeletal density across the Florida Keys Reef Tract despite ongoing warming.

Through the continuous growth of their carbonate skeletons, corals record information about past environmental conditions and their effect on colony fitness. Here, we characterize century-scale growth records of inner and outer reef corals across ~200 km of the Florida Keys Reef Tract (FKRT) using skeletal cores extracted from two ubiquitous reef-building species, Siderastrea siderea and Pseudodiploria strigosa. We find that corals across the FKRT have sustained extension and calcification rates over the past century but have experienced a long-term reduction in skeletal density, regardless of reef zone. Notably, P. strigosa colonies exhibit temporary reef zone-dependent reductions in extension rate corresponding to two known extreme temperature events in 1969-1970 and 1997-1998. We propose that the subtropical climate of the FKRT may buffer corals from chronic growth declines associated with climate warming, though the significant reduction in skeletal density may indicate underlying vulnerability to present and future trends in ocean acidification.
What Is Software? The Role of Empirical Methods in Answering the Question.

This paper explores the potentially pivotal role of Empirical Methods in addressing existential questions about the nature of software. Building upon an earlier paper that asked the question “What is software?”, this paper suggests that a key way to gain such understanding is to ponder the question of how to determine the size of a software entity. The paper notes that there have been a variety of indirect approaches to measuring software size, such as measuring the amount of time taken to produce software, and measuring the number of lines of code in a software entity. But these assume implicitly that such measures correlate positively with the inherent size of the software entity, broadly construed to include the entire panoply of code and non-code artifacts and their interconnections that comprise this entity. As in the original paper, this paper makes the case that entities such as recipes, laws, and processes are types of software, and that learning about their natures illuminates the nature of computer software—and conversely. This paper discusses possible approaches to measuring the size of these other types of artifacts, and uses observations about these approaches to suggest a possible approach to measuring the size of computer software entities. All of this is aimed at making progress in gaining understandings about the nature of software, broadly construed.
A Comprehensive Self-Interference Model for Single-Antenna Full-Duplex Communication Systems

Single-antenna full-duplex communication technology has the potential to substantially increase spectral efficiency. However, limited propagation domain cancellation of single-antenna system results in a higher impact of receiver chain nonlinearities on the residual self-interference (SI) signal. In this paper, we offer a comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase&#x002F;quadrature (IQ) imbalances, phase noise effect, and receiver noise figure. To validate our model, we also propose a more appropriate digital SI cancellation approach considering receiver chain RF and baseband nonlinearities. The proposed technique employs orthogonalization of the design matrix using QR decomposition to alleviate the estimation and cancellation error. Finally, through circuit-level waveform simulation, the performance of the digital cancellation strategy is investigated, which achieves 20 dB more cancellation compared to existing methods.
Path planning for cooperative routing of air-ground vehicles

We consider a cooperative vehicle routing problem for surveillance and reconnaissance missions with communication constraints between the vehicles. We propose a framework which involves a ground vehicle and an aerial vehicle; the vehicles travel cooperatively satisfying the communication limits, and visit a set of targets. We present a mixed integer linear programming (MILP) formulation and develop a branch-and-cut algorithm to solve the path planning problem for the ground and air vehicles. The effectiveness of the proposed approach is corroborated through extensive computational experiments on several randomly generated instances.
Budget-Constrained Multi-Armed Bandits With Multiple Plays.

We study the multi-armed bandit problem with multiple plays and a budget constraint for both the stochastic and the adversarial setting. At each round, exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$). In addition to observing the individual rewards for each arm played, the player also learns a vector of costs which has to be covered with an a-priori defined budget $B$. The game ends when the sum of current costs associated with the played arms exceeds the remaining budget. Firstly, we analyze this setting for the stochastic case, for which we assume each arm to have an underlying cost and reward distribution with support $[c_{\min}, 1]$ and $[0, 1]$, respectively. We derive an Upper Confidence Bound (UCB) algorithm which achieves $O(NK^4 \log B)$ regret. Secondly, for the adversarial case in which the entire sequence of rewards and costs is fixed in advance, we derive an upper bound on the regret of order $O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known $\texttt{Exp3}$ algorithm. We also provide upper bounds that hold with high probability and a lower bound of order $\Omega((1 - K/N)^2 \sqrt{NB/K})$.
Training Faster by Separating Modes of Variation in Batch-Normalized Models

Batch Normalization (BN) is essential to effectively train state-of-the-art deep Convolutional Neural Networks (CNN). It normalizes the layer outputs during training using the statistics of each mini-batch. BN accelerates training procedure by allowing to safely utilize large learning rates and alleviates the need for careful initialization of the parameters. In this work, we study BN from the viewpoint of Fisher kernels that arise from generative probability models. We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution. That means batch normalizing transform can be explained in terms of kernels that naturally emerge from the probability density function that models the generative process of the underlying data distribution. Consequently, it promises higher discrimination power for the batch-normalized mini-batch. However, given the rectifying non-linearities employed in CNN architectures, distribution of the layer outputs show an asymmetric characteristic. Therefore, in order for BN to fully benefit from the aforementioned properties, we propose approximating underlying data distribution not with one, but a mixture of Gaussian densities. Deriving Fisher vector for a Gaussian Mixture Model (GMM), reveals that batch normalization can be improved by independently normalizing with respect to the statistics of disentangled sub-populations. We refer to our proposed soft piecewise version of batch normalization as Mixture Normalization (MN). Through extensive set of experiments on CIFAR-10 and CIFAR-100, using both a 5-layers deep CNN and modern Inception-V3 architecture, we show that mixture normalization reduces required number of gradient updates to reach the maximum test accuracy of the batch-normalized model by $\sim 31\%-47\%$ ∼ 31 % - 47 % across a variety of training scenarios. Replacing even a few BN modules with MN in the 48-layers deep Inception-V3 architecture is sufficient to not only obtain considerable training acceleration but also better final test accuracy. We show that similar observations are valid for 40 and 100-layers deep DenseNet architectures as well. We complement our study by evaluating the application of mixture normalization to the Generative Adversarial Networks (GANs), where “mode collapse” hinders the training process. We solely replace a few batch normalization layers in the generator with our proposed mixture normalization. Our experiments using Deep Convolutional GAN (DCGAN) on CIFAR-10 show that mixture-normalized DCGAN not only provides an acceleration of $\sim 58\%$ ∼ 58 % but also reaches lower (better) “Frechet Inception Distance” (FID) of 33.35 compared to 37.56 of its batch-normalized counterpart.
A unified approach to interpreting model predictions

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
Geometric bounds for Favard length

Given a set in the plane, the average length of its projections over all directions is called Favard length. This quantity measures the size of a set, and is closely related to metric and geometric properties of the set such as rectifiability, Hausdorff dimension, and analytic capacity. In this paper, we develop new geometric techniques for estimating Favard length. We will give a short geometrically motivated proof relating Hausdorff dimension to the decay rate of the Favard length of neighborhoods of a set. We will also show that the sequence of Favard lengths of the generations of a self-similar set is convex; this has direct applications to giving lower bounds on Favard length for various fractal sets.
Hyper-structured illumination

We present a new approach to super-resolution optical imaging, based on structured illumination in hyperbolic media. The proposed system allows for planar geometry, has unlimited field of view, and is robust with respect to optical noise and material losses.
C* Envelopes and the Hao-Ng Isomorphism for Discrete Groups

Using non-selfadjoint techniques, we establish the Hao-Ng isomorphism for the reduced crossed product and all discrete groups. For the full crossed product an analogous result holds for all discrete groups but the C*-correspondences involved have to be hyperrigid. These results are obtained by calculating the C*-envelope of the reduced crossed product of an operator algebra by a discrete group.
Minimax linear estimation at a boundary point

Abstract This paper characterizes the minimax linear estimator of the value of an unknown function at a boundary point of its domain in a Gaussian white noise model under the restriction that the first-order derivative of the unknown function is Lipschitz continuous. The result is then applied to construct the minimax optimal estimator for the regression discontinuity design model, where the parameter of interest involves function values at boundary points.
The consistency and convergence of K-energy minimizing movements

We show that K-energy minimizing movements agree with smooth solutions to Calabi flow as long as the latter exist. As corollaries we conclude that in a general Kahler class long time solutions of Calabi flow minimize both K-energy and Calabi energy. Lastly, by applying convergence results from the theory of minimizing movements, these results imply that long time solutions to Calabi flow converge in the weak distance topology to minimizers of the K-energy functional on the metric completion of the space of Kahler metrics, assuming one exists.
Sound-Word2Vec: Learning Word Representations Grounded in Sounds

To be able to interact better with humans, it is crucial for machines to understand sound – a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic semantic similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream 6textual tasks which require aural grounding. To this end, we propose sound-word2vec – a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering Foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.
Nonempty Intersection of Longest Paths in $2K_2$-Free Graphs

In 1966, Gallai asked whether all longest paths in a connected graph share a common vertex . Counterexamples indicate that this is not true in general. However, Gallai's question is positive for certain well-known classes of connected graphs, such as split graphs, interval graphs, circular arc graphs, outerplanar graphs, and series-parallel graphs. A graph is $2K_2$-free if it does not contain two independent edges as an induced subgraph. In this short note, we show that, in nonempty $2K_2$-free graphs, every vertex of maximum degree is common to all longest paths. Our result implies that all longest paths in a nonempty $2K_2$-free graph have a nonempty intersection. In particular, it strengthens the result on split graphs, as split graphs are $2K_2$-free.
Three conjectures in extremal spectral graph theory

Abstract We prove three conjectures regarding the maximization of spectral invariants over certain families of graphs. Our most difficult result is that the join of P 2 and P n − 2 is the unique graph of maximum spectral radius over all planar graphs . This was conjectured by Boots and Royle in 1991 and independently by Cao and Vince in 1993. Similarly, we prove a conjecture of Cvetkovic and Rowlinson from 1990 stating that the unique outerplanar graph of maximum spectral radius is the join of a vertex and P n − 1 . Finally, we prove a conjecture of Aouchiche et al. from 2008 stating that a pineapple graph is the unique connected graph maximizing the spectral radius minus the average degree. To prove our theorems, we use the leading eigenvector of a purported extremal graph to deduce structural properties about that graph.
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA

This paper presents the FPGA design of a convolutional neural network (CNN) based road segmentation algorithm for real-time processing of LiDAR data. For autonomous vehicles, it is important to perform road segmentation and obstacle detection such that the drivable region can be identified for path planning. Traditional road segmentation algorithms are mainly based on image data from cameras, which is subjected to the light condition as well as the quality of lane markings. LiDAR sensor can obtain the precise 3D geometry information of the vehicle surroundings. However, it is a computational challenge to process a large amount of LiDAR data at real-time. In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data. Furthermore, an efficient hardware design is implemented on the FPGA that can process each LiDAR scan in 16.9 ms, which is much faster than the previous works. Evaluated using KITTI road benchmarks, the proposed solution achieves high accuracy of road segmentation.
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio

Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible perturbations to audio samples. To help researchers and practitioners gain better understanding of the impact of such attacks, and to provide them with tools to help them more easily evaluate and craft strong defenses for their models, we present Adagio, the first tool designed to allow interactive experimentation with adversarial attacks and defenses on an ASR model in real time, both visually and aurally. Adagio incorporates AMR and MP3 audio compression techniques as defenses, which users can interactively apply to attacked audio samples. We show that these techniques, which are based on psychoacoustic principles, effectively eliminate targeted attacks, reducing the attack success rate from 92.5% to 0%. We will demonstrate Adagio and invite the audience to try it on the Mozilla Common Voice dataset. Code related to this paper is available at: https://github.com/nilakshdas/ADAGIO.
Challenges of growing social media networks from the bottom-up through the agent perspective

We develop an agent-based model in order to understand agent/node behaviors that generate social media networks. We use simple rules to synthetically generate a backcloth (friend/follow) network collected using Twitter's API. The Twitter network was collected using seeds for known terrorist propaganda accounts in 2015. Model parameter adjustments were made to reproduce the collected network's summary statistics, stylized facts and general structural measures. We produced an approximate network in line with the general properties of our collected data. We present our findings with a focus on the challenging aspects of this reproduction. We find that while it is possible to generate a social media network utilizing a few simple rules, numerous challenges arise requiring departure from the agent viewpoint and the development of more useful methods. We present numerous weaknesses and challenges in our reproduction and propose potential solutions for future efforts.
MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS.

We tackle the problem of selecting from among a large number of variables those that are “important” for an outcome. We consider situations where groups of variables are also of interest. For example, each variable might be a genetic polymorphism, and we might want to study how a trait depends on variability in genes, segments of DNA that typically contain multiple such polymorphisms. In this context, to discover that a variable is relevant for the outcome implies discovering that the larger entity it represents is also important. To guarantee meaningful results with high chance of replicability, we suggest controlling the rate of false discoveries for findings at the level of individual variables and at the level of groups. Building on the knockoff construction of Barber and Candes [Ann. Statist. 43 (2015) 2055–2085] and the multilayer testing framework of Barber and Ramdas [J. Roy. Statist. Soc. Ser. B 79 (2017) 1247–1268], we introduce the multilayer knockoff filter (MKF). We prove that MKF simultaneously controls the FDR at each resolution and use simulations to show that it incurs little power loss compared to methods that provide guarantees only for the discoveries of individual variables. We apply MKF to analyze a genetic dataset and find that it successfully reduces the number of false gene discoveries without a significant reduction in power.
Hypothesis testing near singularities and boundaries.

The likelihood ratio statistic, with its asymptotic $\chi ^{2}$ distribution at regular model points, is often used for hypothesis testing. However, the asymptotic distribution can differ at model singularities and boundaries, suggesting the use of a $\chi ^{2}$ might be problematic nearby. Indeed, its poor behavior for testing near singularities and boundaries is apparent in simulations, and can lead to conservative or anti-conservative tests. Here we develop a new distribution designed for use in hypothesis testing near singularities and boundaries, which asymptotically agrees with that of the likelihood ratio statistic. For two example trinomial models, arising in the context of inference of evolutionary trees, we show the new distributions outperform a $\chi ^{2}$.
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization

Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of these optimization methods is inhibited by the cost of communicating and synchronizing processors in a parallel setting. Iterative ML methods are particularly sensitive to communication cost since they often require communication every iteration. In this work, we extend well-known techniques from Communication-Avoiding Krylov subspace methods to first-order, block coordinate descent methods for Support Vector Machines and Proximal Least-Squares problems. Our Synchronization-Avoiding (SA) variants reduce the latency cost by a tunable factor of 's' at the expense of a factor of 's' increase in flops and bandwidth costs. We show that the SA-variants are numerically stable and can attain large speedups of up to 5.1x on a Cray XC30 supercomputer.
On the Cheng-Yau gradient estimate for Carnot groups and sub-Riemannian manifolds

In this note we show how results in \cite{BaudoinBonnefont2016, BaudoinGarofalo2013, CoulhonJiangKoskelaSikora2017} yield the Cheng-Yau estimate on two classes of sub-Riemannian manifolds: Carnot groups and sub-Riemannian manifolds satisfying a generalized curvature-dimension inequality.
Progress on high-rate MSR codes: Enabling arbitrary number of helper nodes

This paper presents a construction for high-rate MDS codes that enable bandwidth-efficient repair of a single node. Such MDS codes are also referred to as the minimum storage regenerating (MSR) codes in the distributed storage literature. The construction presented in this paper generates MSR codes for all possible number of helper nodes d as d is a design parameter in the construction. Furthermore, the obtained MSR codes have polynomial sub-packetization (a.k.a. node size) α. The construction is built on the recent code proposed by Sasidharan et al. [1], which works only for d = n — 1, i.e., where all the remaining nodes serve as the helper nodes for the bandwidth-efficient repair of a single node. The results of this paper broaden the set of parameters where the constructions of MSR codes were known earlier.
Cellularity of cyclotomic Birman–Wenzl–Murakami algebras

We show that the cyclotomic Birman-Wenzl-Murakami algebras are cellular by producing a cellular basis of affine tangle diagrams.
Transient frequency control with regional cooperation for power networks

This paper proposes a centralized and a distributed sub-optimal control strategy to maintain in safe regions the real-time transient frequencies of a given collection of buses, and simultaneously preserve asymptotic stability of the entire network. In a receding horizon fashion, the centralized control input is obtained by iteratively solving an open-loop optimization aiming to minimize the aggregate control effort over controllers regulated on individual buses with transient frequency and stability constraints. Due to the non-convexity of the optimization, we propose a convexification technique by identifying a reference control input trajectory. We then extend the centralized control to a distributed scheme, where each sub-controller can only access the state information within a local region. Simulations on a IEEE-39 network illustrate our results.
Local well-posedness for Boltzmann's equation and the Boltzmann hierarchy via Wigner transform

We use the dispersive properties of the linear Schrodinger equation to prove local well-posedness results for the Boltzmann equation and the related Boltzmann hierarchy, set in the spatial domain $${\mathbb{R}^d}$$ for $${d\geq 2}$$ . The proofs are based on the use of the (inverse) Wigner transform along with the spacetime Fourier transform. The norms for the initial data f0 are weighted versions of the Sobolev spaces $${L^2_v H^\alpha_x}$$ with $${\alpha \in \left( \frac{d-1}{2},\infty\right)}$$ . Our main results are local well-posedness for the Boltzmann equation for cutoff Maxwell molecules and hard spheres, as well as local well-posedness for the Boltzmann hierarchy for cutoff Maxwell molecules (but not hard spheres); the latter result holds without any factorization assumption for the initial data.
Qualitative Robustness in Bayesian Inference

The practical implementation of Bayesian inference requires numerical approximation when closed-form expressions are not available. What types of accuracy (convergence) of the numerical approximations guarantee robustness and what types do not? In particular, is the recursive application of Bayes’ rule robust when subsequent data or posteriors are approximated? When the prior is the push forward of a distribution by the map induced by the solution of a PDE, in which norm should that solution be approximated? Motivated by such questions, we investigate the sensitivity of the distribution of posterior distributions (i.e. of posterior distribution-valued random variables, randomized through the data) with respect to perturbations of the prior and data-generating distributions in the limit when the number of data points grows towards infinity.
Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications

Caching popular contents in vehicle-to-vehicle (V2V) communication networks is expected to play an important role in road traffic management, the realization of intelligent transportation systems (ITSs), and the delivery of multimedia content across vehicles. However, for effective caching, the network must dynamically choose the optimal set of cars that will cache popular content and disseminate it in the entire network. However, most of the existing prior art on V2V caching is restricted to cache placement that is solely based on location and user demands and does not account for the large-scale spatio-temporal variations in V2V communication networks. In contrast, in this paper, a novel spatio-temporal caching strategy is proposed based on the notion of temporal graph motifs that can capture spatio-temporal communication patterns in V2V networks. It is shown that, by identifying such V2V motifs, the network can find sub-optimal content placement strategies for effective content dissemination across a vehicular network. Simulation results using real traces from the city of Cologne show that the proposed approach can increase the average data rate by 45% for different network scenarios.
Infinitely many moduli of stability at the dissipative boundary of chaos

In the family of area-contracting Henon-like maps with zero topological entropy we show that there are maps with infinitely many moduli of stability. Thus one cannot find all the possible topological types for non-chaotic area-contracting Henon-like maps in a family with finitely many parameters. A similar result, but for the chaotic maps in the family, became part of the folklore a short time after Henon used such maps to produce what was soon conjectured to be the first non-hyperbolic strange attractor in $\mathbb{R}^2$. Our proof uses recent results about infinitely renormalisable area-contracting Henon-like maps; it suggests that the number of parameters needed to represent all possible topological types for area-contracting Henon-like maps whose sets of periods of their periodic orbits are finite (and in particular are equal to $\{1,\, 2,\dots,\,2^{n-1}\}$ or an initial segment of this $n$-tuple) increases with the number of periods. In comparison, among $C^k$-embeddings of the 2-disk with $k\geq 1$, the maximal moduli number for non-chaotic but non area-contracting maps in the interior of the set of zero-entropy is infinite.
Fixed Points of Maps on the Space of Rational Functions

Given integers s, t, define a function φs,t on the space of all formal series expansions by φs,t( P anx ) = P asn+tx . For each function φs,t, we determine the collection of all rational functions whose Taylor expansions at zero are fixed by φs,t. This collection can be described as a subspace of rational functions whose basis elements correspond to certain s-cyclotomic cosets associated with the pair (s, t). Subject Class: Primary 33
Characterizing Meromorphic Pseudo-lemniscates

Let f be a meromorphic function with simply connected domain $$G\subset \mathbb {C}$$ , and let $$\Gamma \subset \mathbb {C}$$ be a smooth Jordan curve. We call a component of $$f^{-1}(\Gamma )$$ in G a $$\Gamma $$ -pseudo-lemniscate of f. In this note, we give criteria for a smooth Jordan curve $$\mathcal {S}$$ in G (with bounded face D) to be a $$\Gamma $$ -pseudo-lemniscate of f in terms of the number of preimages (counted with multiplicity) which a given w has under f in D (denoted $$\mathcal {N}_f(w)$$ ), as w ranges over the Riemann sphere. As a corollary, we obtain the fact that if $$\mathcal {N}_f(w)$$ takes three different value, then either $$\mathcal {S}$$ contains a critical point of f, or $$f(\mathcal {S})$$ is not a Jordan curve.
Online adaptive principal component analysis and its extensions

We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the underlying environment is changing. Instead, we adopt the adaptive regret metric from the previous literature and propose online adaptive algorithms for PCA and variance minimization, that have sub-linear adaptive regret guarantees. We demonstrate both theoretically and experimentally that the proposed algorithms can adapt to the changing environments.
Generalized Chebyshev polynomials of the second kind

We characterize the generalized Chebyshev polynomials of the second kind (Chebyshev-II), and then we provide a closed form of the generalized Chebyshev-II polynomials using the Bernstein basis. These polynomials can be used to describe the approximation of continuous functions by Chebyshev interpolation and Chebyshev series and how to efficiently compute such approximations. We conclude the paper with some results concerning integrals of the generalized Chebyshev-II and Bernstein polynomials.
Topological recursion for the Poincare polynomial of the combinatorial moduli space of curves

Abstract We show that the Poincare polynomial associated with the orbifold cell decomposition of the moduli space of smooth algebraic curves with distinct marked points satisfies a topological recursion formula of the Eynard–Orantin type. The recursion uniquely determines the Poincare polynomials from the initial data. Our key discovery is that the Poincare polynomial is the Laplace transform of the number of Grothendieck’s dessins d’enfants.
On the energy of inviscid singular flows

Abstract It is known that the energy of a weak solution to the Euler equation is conserved if it is slightly more regular than the Besov space B 3 , ∞ 1 / 3 . When the singular set of the solution is (or belongs to) a smooth manifold, we derive various L p -space regularity criteria dimensionally equivalent to the critical one. In particular, if the singular set is a hypersurface the energy of u is conserved provided the one-sided non-tangential limits to the surface exist and the non-tangential maximal function is L 3 integrable, while the maximal function of the pressure is L 3 / 2 integrable. The results directly apply to prove energy conservation of the classical vortex sheets in both 2D and 3D at least in those cases where the energy is finite.
Dissipativity theory for Nesterov's accelerated method

In this paper, we adapt the control theoretic concept of dissipativity theory to provide a natural understanding of Nesterov's accelerated method. Our theory ties rigorous convergence rate analysis to the physically intuitive notion of energy dissipation. Moreover, dissipativity allows one to efficiently construct Lyapunov functions (either numerically or analytically) by solving a small semidefinite program. Using novel supply rate functions, we show how to recover known rate bounds for Nesterov's method and we generalize the approach to certify both linear and sublinear rates in a variety of settings. Finally, we link the continuous-time version of dissipativity to recent works on algorithm analysis that use discretizations of ordinary differential equations.
THE j-MULTIPLICITY OF MONOMIAL IDEALS

We prove a characterization of the j-multiplicity of a monomial ideal as the normalized volume of a polytopal complex. Our result is an ex- tension of Tessier's volume-theoretic interpretation of the Hilbert-Samuel mul- tiplicity for m-primary monomial ideals. We also give a description of the "-multiplicity of a monomial ideal in terms of the volume of a region.
Shuffling Large Decks of Cards and the Bernoulli–Laplace Urn Model

In card games, in casino games with multiple decks of cards and in cryptography, one is sometimes faced with the following problem: How can a human (as opposed to a computer) shuffle a large deck of cards? The procedure we study is to break the deck into several reasonably sized piles, shuffle each thoroughly, recombine the piles, perform a simple deterministic operation, for instance a cut, and repeat. This process can also be seen as a generalised Bernoulli–Laplace urn model. We use coupling arguments and spherical function theory to derive upper and lower bounds on the mixing times of these Markov chains.
Electric Vehicle Driver Clustering using Statistical Model and Machine Learning

Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties , it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting.
On the maximum number of colorings of a graph

Let $\mathcal{C}_k(n)$ be the family of all connected $k$-chromatic graphs of order $n$. Given a natural number $x\geq k$, we consider the problem of finding the maximum number of $x$-colorings among graphs in $\mathcal{C}_k(n)$. When $k\leq 3$ the answer to this problem is known, and when $k\geq 4$ the problem is wide open. For $k\geq 4$ it was conjectured that the maximum number of $x$-colorings is $x(x-1)\cdots (x-k+1)\,x^{n-k}$. In this article, we prove this conjecture under the additional condition that the independence number of the graphs is at most $2$.
Fast L1-L2 minimization via a proximal operator

This paper aims to develop new and fast algorithms for recovering a sparse vector from a small number of measurements, which is a fundamental problem in the field of compressive sensing (CS). Currently, CS favors incoherent systems, in which any two measurements are as little correlated as possible. In reality, however, many problems are coherent, and conventional methods such as $$L_1$$ minimization do not work well. Recently, the difference of the $$L_1$$ and $$L_2$$ norms, denoted as $$L_1$$ – $$L_2$$ , is shown to have superior performance over the classic $$L_1$$ method, but it is computationally expensive. We derive an analytical solution for the proximal operator of the $$L_1$$ – $$L_2$$ metric, and it makes some fast $$L_1$$ solvers such as forward–backward splitting (FBS) and alternating direction method of multipliers (ADMM) applicable for $$L_1$$ – $$L_2$$ . We describe in details how to incorporate the proximal operator into FBS and ADMM and show that the resulting algorithms are convergent under mild conditions. Both algorithms are shown to be much more efficient than the original implementation of $$L_1$$ – $$L_2$$ based on a difference-of-convex approach in the numerical experiments.
Active Learning Algorithms for Graphical Model Selection

The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years. In many applications such as sensor networks and proteomics it is often expensive to obtain samples from all the variables involved simultaneously. For instance, this might involve the synchronization of a large number of sensors or the tagging of a large number of proteins. To address this important issue, we initiate the study of a novel graphical model selection problem, where the goal is to optimize the total number of scalar samples obtained by allowing the collection of samples from only subsets of the variables. We propose a general paradigm for graphical model selection where feedback is used to guide the sampling to high degree vertices, while obtaining only few samples from the ones with the low degrees. We instantiate this framework with two specific active learning algorithms, one of which makes mild assumptions but is computationally expensive, while the other is more computationally efficient but requires stronger (nevertheless standard) assumptions. Whereas the sample complexity of passive algorithms is typically a function of the maximum degree of the graph, we show that the sample complexity of our algorithms is provable smaller and that it depends on a novel local complexity measure that is akin to the average degree of the graph. We finally demonstrate the efficacy of our framework via simulations.
Optimal execution strategy in the presence of permanent price impact and fixed transaction cost

SUMMARY We study a single risky financial asset model subject to price impact and transaction cost over an infinite horizon. An investor needs to execute a long position in the asset, affecting the price of the asset and possibly incurring a fixed transaction cost. The objective is to maximize the discounted revenue obtained by this transaction. This problem is formulated first as an impulse control problem, and the value function is characterized using the viscosity solutions framework. We also analyze the case where there is no transaction cost and how this formulation relates with a singular control problem. A viscosity solution characterization is provided in this case as well. We also establish a connection between both formulations with zero fixed transaction cost. Numerical examples with different types of price impact conclude the discussion. Copyright © 2011 John Wiley & Sons, Ltd.
Cooperative Adaptive Control for Cloud-Based Robotics

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.
Coherent control of a nitrogen-vacancy center spin ensemble with a diamond mechanical resonator

Coherent control of the nitrogen-vacancy (NV) center in diamond’s triplet spin state has traditionally been accomplished with resonant ac magnetic fields. Here, we show that high-frequency stress resonant with the spin state splitting can also coherently control NV center spins. Because this mechanical drive is parity non-conserving, controlling spins with stress enables direct access to the magnetically forbidden |−1〉↔|+1〉 spin transition. Using a bulk-mode mechanical microresonator fabricated from single-crystal diamond, we apply intense ac stress to the diamond substrate and observe mechanically driven Rabi oscillations between the |−1〉 and |+1〉 states of an NV center spin ensemble. Additionally, we measure the inhomogeneous spin dephasing time (T2*) of the spin ensemble within this {−1,+1} subspace using a mechanical Ramsey sequence and compare it to the dephasing times measured with a magnetic Ramsey sequence for each of the three spin qubit combinations available within the NV center ground state. These results demonstrate coherent control of a spin with a mechanical resonator and could lead to the creation of a phase-sensitive Δ-system inside the NV center ground state with potential applications in quantum optomechanics and metrology.
Equivariant cd-structures and descent theory

Abstract We construct the equivariant version of cd-structures, and we develop descent theory for topologies coming from equivariant cd-structures. In particular, we reprove several results of Cisinski–Deglies on etale descent, qfh-descent, and h-descent. Since the etale topos, qfh-topos, and h-topos do not come from usual cd-structures, such results cannot be produced by usual cd-structures. We also apply equivariant cd-structures to study several topologies on the category of noetherian fs log schemes.