cssi/SciSciGPT-SciSciNet huggingface dataset:
Estimation of the Szlenk index of reflexive Banach spaces using generalized Baernstein spaces

For each ordinal $\alpha< \omega_1$, we prove the existence of a separable, reflexive Banach space with a basis and Szlenk index $\omega^{\alpha+1}$ which is universal for the class of separable, reflexive Banach spaces $X$ such that the Szlenk indices $Sz(X), Sz(X^*)$ do not exceed $\omega^\alpha$.
Maximizing H‐Colorings of Connected Graphs with Fixed Minimum Degree

For graphs G and H, an H-coloring of G is a map from the vertices of G to the vertices of H that preserves edge adjacency. We consider the following extremal enumerative question: for a given H, which connected n-vertex graph with minimum degree δ maximizes the number of H-colorings? We show that for nonregular H and sufficiently large n, the complete bipartite graph Kδ,n−δ is the unique maximizer. As a corollary, for nonregular H and sufficiently large n the graph Kk,n−k is the unique k-connected graph that maximizes the number of H-colorings among all k-connected graphs. Finally, we show that this conclusion does not hold for all regular H by exhibiting a connected n-vertex graph with minimum degree δ that has more Kq-colorings (for sufficiently large q and n) than Kδ,n−δ.
Power-Efficient Deployment of UAVs as Relays

Optimal deployment of unmanned aerial vehicles (UAVs) as communication relays is studied for fixed-rate variable-power systems. The considered setup is a set of ground transmitters (G Ts) wishing to communicate with a set of ground receivers (GRs) through the UAVs. Each GT-GR pair communicates through only one selected UAV and have no direct link. Two different UAV selection scenarios are studied: In centralized selection, a decision center assigns an optimal UAV depending on the locations of all terminals. In distributed selection, a G T selects its relaying UAV using only the local knowledge of its distances to the UAV s. For both selection scenarios, the optimal tradeoff between the UAV and GT power consumptions are determined using tools from quantization theory. Specifically, the two extremal regimes of one UAV and very large number of UAVs are analyzed for a path loss exponent of 2. Numerical optimization of UAV locations are also discussed. Simulations are provided to confirm the analytical findings.
Integral models of Shimura varieties with parahoric level structure

For a prime $p > 2$ , we construct integral models over $p$ for Shimura varieties with parahoric level structure, attached to Shimura data $(G,X)$ of abelian type, such that $G$ splits over a tamely ramified extension of ${\mathbf {Q}}_{\,p}$ . The local structure of these integral models is related to certain “local models”, which are defined group theoretically. Under some additional assumptions, we show that these integral models satisfy a conjecture of Kottwitz which gives an explicit description for the trace of Frobenius action on their sheaf of nearby cycles.
Modeling Semantic Plausibility by Injecting World Knowledge

Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches

The ability to automatically recognize a person’s behavioral context can contribute to health monitoring, aging care, and many other domains. Validating context recognition in the wild is crucial to promote practical applications that work in real-life settings. The authors collected more than 300,000 minutes of sensor data with context labels from 60 subjects. Unlike previous studies, these subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that were harder to recognize. The authors demonstrate how fusion of multimodal sensors is important for resolving such cases. They present a baseline system and encourage researchers to use their public dataset to compare methods and improve context recognition in the wild.
Where Graph Topology Matters: The Robust Subgraph Problem.

Robustness is a critical measure of the resilience of large networked systems, such as transportation and communication networks. Most prior works focus on the global robustness of a given graph at large, e.g., by measuring its overall vulnerability to external attacks or random failures. In this paper, we turn attention to local robustness and pose a novel problem in the lines of subgraph mining: given a large graph, how can we find its most robust local subgraph (RLS)? We define a robust subgraph as a subset of nodes with high communicability [15] among them, and formulate the RLS-PROBLEM of finding a subgraph of given size with maximum robustness in the host graph. Our formulation is related to the recently proposed general framework [39] for the densest subgraph problem, however differs from it substantially in that besides the number of edges in the subgraph, robustness also concerns with the placement of edges, i.e., the subgraph topology. We show that the RLSPROBLEM is NP-hard and propose two heuristic algorithms based on top-down and bottom-up search strategies. Further, we present modifications of our algorithms to handle three practical variants of the RLS-PROBLEM. Experiments on synthetic and real-world graphs demonstrate that we find subgraphs with larger robustness than the densest subgraphs [9, 39] even at lower densities, suggesting that the existing approaches are not suitable for the new problem setting.
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation

Abstract We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: it involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work we collect and annotate a new data set of continuous fingerspelling videos, compare several types of recognizers, and explore the problem of signer variation. Our best-performing models are segmental (semi-Markov) conditional random fields using deep neural network-based features. In the signer-dependent setting, our recognizers achieve up to about 92% letter accuracy. The multi-signer setting is much more challenging, but with neural network adaptation we achieve up to 83% letter accuracies in this setting.
Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation

We present the opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things IoT. We devise a novel adaptive Bloom filter mechanism to efficiently encode the downward source route in OSR, which significantly reduces the length of the source route field in the packet header. Moreover, each node in the network stores only the set of its direct children. Thus, OSR is scalable to very large-size WSN/IoT deployments. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node’s upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy networks (LLNs). Our evaluation of OSR via both simulations and real-world testbed experiments demonstrates its merits in comparison with the state-of-the-art protocols.
Connections on equivariant Hamiltonian Floer cohomology

We construct connections on $S^1$-equivariant Hamiltonian Floer cohomology, which differentiate with respect to certain formal parameters.
Sparse Sampling of Water Density Fluctuations in Interfacial Environments

The free energetics of water density fluctuations near a surface, and the rare low-density fluctuations in particular, serve as reliable indicators of surface hydrophobicity; the easier it is to displace the interfacial waters, the more hydrophobic the underlying surface is. However, characterizing the free energetics of such rare fluctuations requires computationally expensive, non-Boltzmann sampling methods like umbrella sampling. This inherent computational expense associated with umbrella sampling makes it challenging to investigate the role of polarizability or electronic structure effects in influencing interfacial fluctuations. Importantly, it also limits the size of the volume, which can be used to probe interfacial fluctuations. The latter can be particularly important in characterizing the hydrophobicity of large surfaces with molecular-level heterogeneities, such as those presented by proteins. To overcome these challenges, here we present a method for the sparse sampling of water density fluct...
Optimal transport for seismic full waveform inversion

Full waveform inversion is a successful procedure for determining properties of the earth from surface measurements in seismology. This inverse problem is solved by a PDE constrained optimization where unknown coefficients in a computed wavefield are adjusted to minimize the mismatch with the measured data. We propose using the Wasserstein metric, which is related to optimal transport, for measuring this mismatch. Several advantageous properties are proved with regards to convexity of the objective function and robustness with respect to noise. The Wasserstein metric is computed by solving a Monge-Ampere equation. We describe an algorithm for computing its Frechet gradient for use in the optimization. Numerical examples are given.
Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms.

Abstract Local field potential (LFP) oscillations are primarily shaped by the superposition of postsynaptic currents. Hippocampal LFP oscillations in the 25- to 50-Hz range (“slow γ”) are proposed to support memory retrieval independent of other frequencies. However, θ harmonics extend up to 48 Hz, necessitating a study to determine whether these oscillations are fundamentally the same. We compared the spectral analysis methods of wavelet, ensemble empirical-mode decomposition (EEMD), and Fourier transform. EEMD, as previously applied, failed to account for the θ harmonics. Depending on analytical parameters selected, wavelet may convolve over high-order θ harmonics due to the variable time-frequency atoms, creating the appearance of a broad 25- to 50-Hz rhythm. As an illustration of this issue, wavelet and EEMD depicted slow γ in a synthetic dataset that only contained θ and its harmonics. Oscillatory transience cannot explain the difference in approaches as Fourier decomposition identifies ripples triggered to epochs of high-power, 120- to 250-Hz events. When Fourier is applied to high power, 25- to 50-Hz events, only θ harmonics are resolved. This analysis challenges the identification of the slow γ rhythm as a unique fundamental hippocampal oscillation. While there may be instances in which slow γ is present in the rat hippocampus, the analysis presented here shows that unless care is exerted in the application of EEMD and wavelet techniques, the results may be misleading, in this case misrepresenting θ harmonics. Moreover, it is necessary to reconsider the characteristics that define a fundamental hippocampal oscillation as well as theories based on multiple independent γ bands.
Fractional minimal rank

The notion of fractional minimal rank of a partial matrix is introduced, a quantity that lies between the triangular minimal rank and the minimal rank of a partial matrix. The fractional minimal ra...
A Dirty Model for Multiple Sparse Regression

The task of sparse linear regression consists of finding an unknown sparse vector from linear measurements. Solving this task even under “high-dimensional” settings, where the number of samples is fewer than the number of variables, is now known to be possible via methods such as the LASSO. We consider the multiple sparse linear regression problem, where the task consists of recovering several related sparse vectors at once. A simple approach to this task would involve solving independent sparse linear regression problems, but a natural question is whether one can reduce the overall number of samples required by leveraging partial sharing of the support sets, or nonzero patterns, of the signal vectors. A line of recent research has studied the use of l1/lq norm block-regularizations with q > 1 for such problems. However, depending on the level of sharing, these could actually perform worse in sample complexity when compared to solving each problem independently. We present a new “adaptive” method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. We show how to achieve this using a very simple idea: decompose the parameters into two components and regularize these differently. We show, theoretically and empirically, that our method strictly and noticeably outperforms both l1 or l1/lq methods, over the entire range of possible overlaps (except at boundary cases, where we match the best method), even under high-dimensional scaling.
How to construct a Hovey triple from two cotorsion pairs

Let $\mathcal{A}$ be an abelian category, or more generally a weakly idempotent complete exact category, and suppose we have two complete hereditary cotorsion pairs $(\mathcal{Q}, \widetilde{\mathcal{R}})$ and $(\widetilde{\mathcal{Q}}, \mathcal{R})$ in $\mathcal{A}$ satisfying $\widetilde{\mathcal{R}} \subseteq \mathcal{R}$ and $\mathcal{Q} \cap \widetilde{\mathcal{R}} = \widetilde{\mathcal{Q}} \cap \mathcal{R}$. We show how to construct a (necessarily unique) abelian model structure on $\mathcal{A}$ with $\mathcal{Q}$ (respectively $\widetilde{\mathcal{Q}}$) as the class of cofibrant (resp. trivially cofibrant) objects and $\mathcal{R}$ (respectively $\widetilde{\mathcal{R}}$) as the class of fibrant (resp. trivially fibrant) objects.
Hierarchy of modular graph identities

The low energy expansion of Type II superstring amplitudes at genus one is organized in terms of modular graph functions associated with Feynman graphs of a conformal scalar field on the torus. In earlier work, surprising identities between two-loop graphs at all weights, and between higher-loop graphs of weights four and five were constructed. In the present paper, these results are generalized in two complementary directions. First, all identities at weight six and all dihedral identities at weight seven are obtained and proven. Whenever the Laurent polynomial at the cusp is available, the form of these identities confirms the pattern by which the vanishing of the Laurent polynomial governs the full modular identity. Second, the family of modular graph functions is extended to include all graphs with derivative couplings and worldsheet fermions. These extended families of modular graph functions are shown to obey a hierarchy of inhomogeneous Laplace eigenvalue equations. The eigenvalues are calculated analytically for the simplest infinite sub-families and obtained by Maple for successively more complicated sub-families. The spectrum is shown to consist solely of eigenvalues s(s − 1) for positive integers s bounded by the weight, with multiplicities which exhibit rich representation-theoretic patterns.
Deep Cross Polarimetric Thermal-to-Visible Face Recognition

In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The proposed architecture is able to make full use of the polarimetric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recognition methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embedding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superiority of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.
The density of dark matter in the Galactic bulge and implications for indirect detection

Abstract A recent study by Portail et al., which made use of the number of horizontal branch stars observed in infrared photometric surveys and kinematic measurements of M-giant stars from the BRAVA survey combined with N-body simulations of stellar populations, has presented a new determination of the dark matter mass within the bulge-bar region of the Milky Way. That study constrains the total mass within the ± 2 . 2 × ± 1 . 4 × ± 1 . 2 kpc volume of the bulge-bar region to be ( 1 . 84 ± 0 . 07 ) × 1 0 10 M ? , of which 9%–30% is made up of dark matter. Here, we use this result to constrain the Milky Way’s dark matter density profile, and discuss the implications for indirect dark matter searches. Although uncertainties remain significant, these results are consistent with and generally favor dark matter distributions with a cusped density profile. For example, for a scale radius of 20 kpc and a local dark matter density of 0.4 GeV/cm 3 , density profiles with an inner slope of 0.69 to 1.40 are favored, approximately centered around the standard NFW value. In contrast, profiles with large (multi-kiloparsec) flat-density cores are disfavored by this information.
Deep Image Super Resolution via Natural Image Priors

Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples. Most existing deep networks for SR produce high quality results when training data is abundant. However, their performance degrades sharply when training is limited. We propose to regularize deep structures with prior knowledge about the images so that they can capture more structural information from the same limited data. In particular, we incorporate in a tractable fashion within the CNN framework, natural image priors which have shown to have much recent success in imaging and vision inverse problems. Experimental results show that the proposed deep network with natural image priors is particularly effective in training starved regimes.
A Compact CMOS Memristor Emulator Circuit and its Applications

Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic circuits. We introduce a compact CMOS circuit that emulates idealized memristor characteristics and can bridge the gap between concepts to chip-scale realization by transcending device challenges. The CMOS memristor circuit embodies a two-terminal variable resistor whose resistance is controlled by the voltage applied across its terminals. The memristor ’state’ is held in a capacitor that controls the resistor value. This work presents the design and simulation of the memristor emulation circuit. Furthermore, the memristor emulator circuit can be designed and fabricated using standard commercial CMOS technologies and opens doors to interesting applications in neuromorphic and machine learning circuits.
Covariant functors and asymptotic stability

Abstract Let R be a commutative Noetherian ring, I , J ideals of R and M a finitely generated R-module. Let F be a covariant R-linear functor from the category of finitely generated R-modules to itself. We first show that if F is coherent, then the sets Ass R F ( M / I n M ) , Ass R F ( I n − 1 M / I n M ) and the values depth J F ( M / I n M ) , depth J F ( I n − 1 M / I n M ) become independent of n for large n. Next, we consider several examples in which F is a rather familiar functor, but is not coherent or not even finitely generated in general. In these cases, the sets Ass R F ( M / I n M ) still become independent of n for large n. We then show one negative result where F is not finitely generated. Finally, we give a positive result where F belongs to a special class of functors which are not finitely generated in general, an example of which is the zeroth local cohomology functor.
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and character-level models in each particular case was informed through validation performance. Our final system is a combination of classifiers using word-level or character-level models. We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust. Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively.
Linking Cellular and Mechanical Processes in Articular Cartilage Lesion Formation: A Mathematical Model.

Post-traumatic osteoarthritis affects almost 20\% of the adult US population. An injurious impact applies a significant amount of physical stress on articular cartilage and can initiate a cascade of biochemical reactions that can lead to the development of osteoarthritis. In our effort to understand the underlying biochemical mechanisms of this debilitating disease, we have constructed a multiscale mathematical model of the process with three components: cellular, chemical, and mechanical. The cellular component describes the different chondrocyte states according to the chemicals these cells release. The chemical component models the change in concentrations of those chemicals. The mechanical component contains a simulation of a blunt impact applied onto a cartilage explant and the resulting strains that initiate the biochemical processes. The scales are modeled through a system of partial-differential equations and solved numerically. The results of the model qualitatively capture the results of laboratory experiments of drop-tower impacts on cartilage explants. The model creates a framework for incorporating explicit mechanics, simulated by finite element analysis, into a theoretical biology framework. The effort is a step toward a complete virtual platform for modeling the development of post-traumatic osteoarthritis, which will be used to inform biomedical researchers on possible non-invasive strategies for mitigating the disease.
Learning Unified Embedding for Apparel Recognition

In apparel recognition, deep neural network models are often trained separately for different verticals (e.g. [7]). However, using specialized models for different verticals is not scalable and expensive to deploy. This paper addresses the problem of learning one unified embedding model for multiple object verticals (e.g. all apparel classes) without sacrificing accuracy. The problem is tackled from two aspects: training data and training difficulty. On the training data aspect, we figure out that for a single model trained with triplet loss, there is an accuracy sweet spot in terms of how many verticals are trained together. To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss. This new loss makes the training easier and make it possible for more efficient use of the feature space. The end result is a unified model which can achieve the same retrieval accuracy as a number of separate specialized models, while having the model complexity as one.
Sustainability: Scholarly repository as an enterprise

The expanding need for an open information sharing infrastructure to promote scholarly communication led to the pioneering establishment of arXiv.org, now maintained by the Cornell University Library. To be sustainable, the repository requires careful, long term planning for services, management and funding. The library is developing a sustainability model for arXiv.org, based on voluntary contributions and the ongoing participation and support of 200 libraries and research laboratories around the world. The sustainability initiative is based on a membership model and builds on arXiv's technical, service, financial and policy infrastructure. Five principles for sustainability drive development, starting with deep integration into the scholarly community. Also key are a clearly defined mandate and governance structure, a stable yet innovative technology platform, systematic creation of content policies and strong business planning strategies. Repositories like arXiv must consider usability and life cycle alongside values and trends in scholarly communication. To endure, they must also support and enhance their service by securing and managing resources and demonstrating responsible stewardship.
Hausdorff dimension of divergent diagonal geodesics on product of finite-volume hyperbolic spaces

In this paper, we consider the product space of several non-compact finite-volume hyperbolic spaces, . This extends a result of Cheung.
The geometry of uniserial representations of finite dimensional algebras. III: Finite uniserial type

A description is given of those sequences ${\Bbb S}= (S(0),S(1),\dots,S(l))$ of simple modules over a finite dimensional algebra for which there are only finitely many uniserial modules with consecutive composition factors $S(0),\dots,S(l)$. Necessary and sufficient conditions for an algebra to permit only a finite number of isomorphism types of uniserial modules are derived. The main tools in this investigation are the affine algebraic varieties parametrizing the uniserial modules with composition series ${\Bbb S}$.
Achieving delay rate-function optimality in OFDM downlink with time-correlated channels

There have been recent attempts to develop scheduling schemes for downlink transmission in a single cell of a multi-channel (e.g., OFDM-based) cellular network. These works have been quite promising in that they have developed low-complexity index scheduling policies that are delay-optimal (in a large deviation rate-function sense). However, these policies require that the channel is ON or OFF in each time-slot with a fixed probability (i.e., there is no memory in the system), while the reality is that due to channel fading and doppler shift, channels are often time-correlated in these cellular systems. Thus, an important open question is whether one can find simple index scheduling policies that are delay-optimal even when the channels are time-correlated. In this paper, we attempt to answer this question for time-correlated ON/OFF channels. In particular, we show that the class of oldest packets first (OPF) policies that give a higher priority to packets with a large delay is delay rate-function optimal under two conditions: 1) The channel is non-negatively correlated, and 2) The distribution of the OFF period is geometric. We use simulations to further elucidate the theoretical results.
UnrealCV: Connecting Computer Vision to Unreal Engine

Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (Project website: http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.
Sasaki–Einstein metrics on a class of 7-manifolds

Abstract In this note we give an explicit construction of Sasaki–Einstein metrics on a class of simply connected 7-manifolds with the rational cohomology of the 2-fold connected sum of S 2 × S 5 . The homotopy types are distinguished by torsion in H 4 .
Nonparametric Bayesian Negative Binomial Factor Analysis

A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capturing the tendency for a covariate present in a sample to both repeat itself and excite related ones. To address this limitation, we construct negative binomial factor analysis (NBFA) to factorize the matrix under the negative binomial likelihood, and relate it to a Dirichlet-multinomial distribution based mixed-membership model. To support countably infinite factors, we propose the hierarchical gamma-negative binomial process. By exploiting newly proved connections between discrete distributions, we construct two blocked and a collapsed Gibbs sampler that all adaptively truncate their number of factors, and demonstrate that the blocked Gibbs sampler developed under a compound Poisson representation converges fast and has low computational complexity. Example results show that NBFA has a distinct mechanism in adjusting its number of inferred factors according to the sample lengths, and provides clear advantages in parsimonious representation, predictive power, and computational complexity over previously proposed discrete latent variable models, which either completely ignore burstiness, or model only the burstiness of the covariates but not that of the factors.
Nodal lengths of eigenfunctions in the disc

In this paper, we derive the sharp lower and upper bounds of nodal lengths of Laplacian eigenfunctions in the disc. Furthermore, we observe a geometric property of the eigenfunctions whose nodal curves maximize the nodal length.
On automorphisms of biproducts

We study certain subgroups of the full group of Hopf algebra automorphisms of a biproduct. In the process, interesting subgroups of certain permutation groups come into play.
Five-dimensional maximally supersymmetric Yang-Mills in supergravity backgrounds

We determine the action for five-dimensional maximally supersymmetric Yang-Mills in off-shell supergravity backgrounds. The resulting theory contains novel five-dimensional BF type couplings as well as cubic scalar interactions which vanish in flat space.
The Muon $g$-$2$ Experiment at Fermilab

A new measurement of the anomalous magnetic moment of the muon, aμ ≡ (g – 2)/2 will be performed at the Fermi National Accelerator Laboratory with data taking beginning in 2017. The most recent measurement, performed at Brookhaven National Laboratory (BNL) and completed in 2001, shows a 3.5 standard deviation discrepancy with the standard model value of aμ. The new measurement will accumulate 21 times the BNL statistics using upgraded magnet, detector, and storage ring systems, enabling a measurement of aμ to 140 ppb, a factor of 4 improvement in the uncertainty the previous measurement. This improvement in precision, combined with recent improvements in our understanding of the QCD contributions to the muon g-2, could provide a discrepancy from the standard model greater than 7σ if the central value is the same as that measured by the BNL experiment, which would be a clear indication of new physics.
De-identification of patient notes with recurrent neural networks.

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of electronic health record databases, the limited number of researchers with access to non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall of 97.38 and a precision of 98.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall of 99.25 and a precision of 99.21. Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no manual feature engineering.
Sorting Permutations: Games, Genomes, and Cycles

Permutation sorting, one of the fundamental steps in pre-processing data for the efficient application of other algorithms, has a long history in mathematical research literature and has numerous applications. Two special-purpose sorting operations are considered in this paper: context directed swap, (cds) and context directed reversal, (cdr). These are special cases of sorting operations that were studied in prior work on permutation sorting. Moreover, cds and cdr have been postulated to model molecular sorting events that occur in the genome maintenance program of certain species of single-celled organisms called ciliates. This paper investigates mathematical aspects of these two sorting operations. The main result of this paper is a generalization of previously discovered characterizations of cds-sortability of a permutation. The combinatorial structure underlying this generalization suggests natural combinatorial two-player games. These games are the main mathematical innovation of this paper.
Riemann Hypothesis for DAHA superpolynomials and plane curve singularities

Stable Khovanov-Rozansky polynomials of algebraic knots are expected to coincide with certain generating functions, superpolynomials, of nested Hilbert schemes and flagged Jacobian factors of the corresponding plane curve singularities. Also, these 3 families conjecturally match the DAHA superpolynomials. These superpolynomials can be considered as singular counterparts and generalizations of the Hasse-Weil zeta-functions. We conjecture that all $a$-coefficients of the DAHA superpolynomials upon the substitution $q\mapsto qt$ satisfy the Riemann Hypothesis for sufficiently small $q$ for uncolored algebraic knots, presumably for $q\le 1/2$ as $a=0$. This can be partially extended to algebraic links at least for $a=0$. Colored links are also considered, though mostly for rectangle Young diagrams. Connections with Kapranov's motivic zeta and the Galkin-Stohr zeta-functions are discussed.
Aperture Supervision for Monocular Depth Estimation

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.
The connectedness of hessenberg varieties

Abstract In this paper we consider certain closed subvarieties of the flag variety, known as Hessenberg varieties. These varieties arise in representation theory, algebraic geometry, and combinatorics. We give a connectedness criterion for semisimple Hessenberg varieties that generalizes a criterion given by Anderson and Tymoczko. It also generalizes results of Iveson in type A which prove that all Hessenberg varieties satisfying this criterion are connected. We then show that nilpotent Hessenberg varieties are rationally connected.
Expansions of subfields of the real field by a discrete set

Let K be a subfield of the real field, D be a discrete subset of K and f : D^n -> K be a function such that f(D^n) is somewhere dense. Then (K,f) defines the set of integers. We present several applications of this result. We show that K expanded by predicates for different cyclic multiplicative subgroups defines the set of integers. Moreover, we prove that every definably complete expansion of a subfield of the real field satisfies an analogue of the Baire Category Theorem.
The Accretion of Fuel at the Disk-Halo Interface

We discuss the support for the cooling of gas directly at the disk-halo interface as a mechanism to continually fuel a galaxy. This may be an important reservoir as there is not enough cold gas observed in galaxy halos and simulations indicate the existing cold clouds will be rapidly destroyed as they move through the surrounding halo medium. We show possible evidence for a net infall of the WIM layer in the Milky Way, simulation results showing the recooling of warm clouds at the disk-halo interface, and GALFA HI data of small, cold HI clouds that could represent this recooling.
INTERSECTIONS OF FINITE FAMILIES OF FINITE INDEX SUBFACTORS

We prove that finiteness of the index of the intersection of a finite set of finite index subalgebras in a von Neumann algebra (with small centre) is equivalent to the finite dimensionality of the algebra generated by the conditional expectations onto the subalgebras.
Is language evolution grinding to a halt? The scaling of lexical turbulence in English fiction suggests it is not

Abstract Of basic interest is the quantification of the long term growth of a language's lexicon as it develops to more completely cover both a culture's communication requirements and knowledge space. Here, we explore the usage dynamics of words in the English language as reflected by the Google Books 2012 English Fiction corpus. We critique an earlier method that found decreasing birth and increasing death rates of words over the second half of the 20th Century, showing death rates to be strongly affected by the imposed time cutoff of the arbitrary present and not increasing dramatically. We provide a robust, principled approach to examining lexical evolution by tracking the volume of word flux across various relative frequency thresholds. We show that while the overall statistical structure of the English language remains stable over time in terms of its raw Zipf distribution, we find evidence of an enduring ‘lexical turbulence’: The flux of words across frequency thresholds from decade to decade scales superlinearly with word rank and exhibits a scaling break we connect to that of Zipf's law. To better understand the changing lexicon, we examine the contributions to the Jensen-Shannon divergence of individual words crossing frequency thresholds. We also find indications that scholarly works about fiction are strongly represented in the 2012 English Fiction corpus, and suggest that a future revision of the corpus should attempt to separate critical works from fiction itself.
Geometry of Thin Films

We study ray optics in the context of double-mirror systems, in the limit as the two mirrors approach one another (thin films). This leads to a novel set of differential equations on a mirror surface which have interesting structure as seen from the perspective of symplectic geometry and hamiltonian mechanics.
Traffic optimization to control epidemic outbreaks in metapopulation models

We propose a novel framework to study viral spreading processes in metapopulation models. Large subpopulations (i.e., cities) are connected via metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic infrastructure). The problem of containing the propagation of an epidemic outbreak in a metapopulation model by controlling the traffic between subpopulations is considered. Controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. Based on this spectral condition, we propose a convex optimization framework to find cost-optimal approaches to traffic control in epidemic outbreaks.
Automatically generating commit messages from diffs using neural machine translation

Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically ``translate'' diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.
Critical points of master functions and flag varieties

We consider critical points of master functions associated with integral dominant weights of Kac–Moody algebras and introduce a generating procedure constructing new critical points starting from a given one. The set of all critical points constructed from a given one is called a population. We formulate a conjecture that a population is isomorphic to the flag variety of the Langlands dual Kac–Moody algebra and prove the conjecture for algebras slN+1, so2N+1, and sp2N. We show that populations associated with a collection of integral dominant slN+1-weights are in one to one correspondence with intersection points of suitable Schubert cycles in a Grassmannian variety.
An RNAi Screen for Genes Required for Growth of Drosophila Wing Tissue.

Cell division and tissue growth must be coordinated with development. Defects in these processes are the basis for a number of diseases, including developmental malformations and cancer. We have conducted an unbiased RNAi screen for genes that are required for growth in the Drosophila wing, using GAL4-inducible short hairpin RNA (shRNA) fly strains made by the Drosophila RNAi Screening Center. shRNA expression down the center of the larval wing disc using dpp-GAL4, and the central region of the adult wing was then scored for tissue growth and wing hair morphology. Out of 4,753 shRNA crosses that survived to adulthood, 18 had impaired wing growth. FlyBase and the new Alliance of Genome Resources knowledgebases were used to determine the known or predicted functions of these genes and the association of their human orthologs with disease. The function of eight of the genes identified has not been previously defined in Drosophila. The genes identified included those with known or predicted functions in cell cycle, chromosome segregation, morphogenesis, metabolism, steroid processing, transcription, and translation. All but one of the genes are similar to those in humans, and many are associated with disease. Knockdown of lin-52, a subunit of the Myb-MuvB transcription factor, or βNACtes6, a gene involved in protein folding and trafficking, resulted in a switch from cell proliferation to an endoreplication growth program through which wing tissue grew by an increase in cell size (hypertrophy). It is anticipated that further analysis of the genes that we have identified will reveal new mechanisms that regulate tissue growth during development.
Capturing strain stiffening using Volume Controlled Cavity Expansion

Abstract Strain-stiffening is a well-documented behavior in soft biological materials such as liver and brain tissue. Measuring and characterizing this nonlinear response, which is commonly considered as a mechanism for damage prevention, is of great interest to engineers for design of better biomimetic materials, and to physicians for diagnostic purposes. However, probing the elastic response of soft or biological materials at large deformation in their natural habitat, is an arduous task. Here, we present the Volume Controlled Cavity Expansion (VCCE) technique as a measurement method that offers the ability of characterizing the local stiffening response of materials in addition to identifying their shear modulus. By employing minimal constitutive representations involving only two constants (Mooney–Rivlin, Gent, and Ogden) we show that for the conventional PDMS samples, this technique and an accompanying data analysis method capture the shear modulus, as well as providing reliable measures of the stiffening behavior of the samples.
Family income from birth through adolescence: Implications for positive youth development

Abstract This study took a life course approach to examine associations among family income from birth to age 15, and adolescent health and well-being. Utilizing latent growth mixture modeling, we identified four distinct family income trajectories based on changes in low-income status (family income ≤200% of the federal poverty line) over 15 years, which in turn related to their outcomes in adolescence. More specifically, youth living in a consistent state of higher income from birth to age 15 (Consistent Higher Income) reported better health and behavioral outcomes than youth in the Consistent Low Income, Increasing Income, or Decreasing Income groups. Furthermore, despite tending to have relatively high-income levels at the beginning of life, the Decreasing Income group showed several risky behavioral and health patterns, including more sexual risk-taking and high blood pressure. Results underscore the importance of studying changes in family income across childhood and adolescence.