cssi/SciSciGPT-SciSciNet huggingface dataset:
Presentations of topological full groups by generators and relations

Abstract We describe generators and defining relations for the commutator subgroup of topological full groups of minimal subshifts. We show that the word problem in a topological full group is solvable if and only if the language of the underlying subshift is recursive.
Optimal control in Markov decision processes via distributed optimization

Optimal control synthesis in stochastic systems with respect to quantitative temporal logic constraints can be formulated as linear programming problems. However, centralized synthesis algorithms do not scale to many practical systems. To tackle this issue, we propose a decomposition-based distributed synthesis algorithm. By decomposing a large-scale stochastic system modeled as a Markov decision process into a collection of interacting sub-systems, the original control problem is formulated as a linear programming problem with a sparse constraint matrix, which can be solved through distributed optimization methods. Additionally, we propose a decomposition algorithm which automatically exploits, if it exists, the modular structure in a given large-scale system. We illustrate the proposed methods through robotic motion planning examples.
Round spheres are Hausdorff stable under small perturbation of entropy

We show that if the entropy of any closed hypersurface is close to that of a round hyper-sphere, then it is close to a round sphere in Hausdorff distance. Generalizing the result of \cite{BW1} to higher dimensions.
Stochastic areas, winding numbers and Hopf fibrations

We define and study stochastic areas processes associated with Brownian motions on the complex symmetric spaces \(\mathbb {CP}^n\) and \(\mathbb {CH}^n\). The characteristic functions of those processes are computed and limit theorems are obtained. In the case \(n=1\), we also study windings of the Brownian motion on those spaces and compute the limit distributions. For \(\mathbb {CP}^n\) the geometry of the Hopf fibration plays a central role, whereas for \(\mathbb {CH}^n\) it is the anti-de Sitter fibration.
Graphs with Minimal Well-Covered Dimension

We generalize a theorem by Brown and Nowakowski on the well-covered dimension of chordal graphs. Furthermore, we prove that the well-covered dimension of any Sierpinski gasket graph of order at least 2 is equal to 3, the simplicial clique number. Acknowledgements: The author gratefully acknowledges support from NSF Grant #DMS-1156273, the California State University Fresno Mathematics REU program, especially Oscar Vega and Tamas Forgacs, the Mellon Mays Undergraduate Fellowship, and the editor and referee for their kind suggestions. Page 38 RHIT Undergrad. Math. J., Vol. 15, No. 2
Using network motifs to characterize temporal network evolution leading to diffusion inhibition

Network motifs are patterns of over-represented node interactions in a network which have been previously used as building blocks to understand various aspects of the social networks. In this paper, we use motif patterns to characterize the information diffusion process in social networks. We study the lifecycle of information cascades to understand what leads to saturation of growth in terms of cascade reshares, thereby resulting in expiration, an event we call “diffusion inhibition”. In an attempt to understand what causes inhibition, we use motifs to dissect the network obtained from information cascades coupled with traces of historical diffusion or social network links. Our main results follow from experiments on a dataset of cascades from the Weibo platform and the Flixster movie ratings. We observe the temporal counts of 5-node undirected motifs from the cascade temporal networks leading to the inhibition stage. Empirical evidences from the analysis lead us to conclude the following about stages preceding inhibition: (1) individuals tend to adopt information more from users they have known in the past through social networks or previous interactions thereby creating patterns containing triads more frequently than acyclic patterns with linear chains and (2) users need multiple exposures or rounds of social reinforcement for them to adopt an information and as a result information starts spreading slowly thereby leading to the death of the cascade. Following these observations, we use motif-based features to predict the edge cardinality of the network exhibited at the time of inhibition. We test features of motif patterns using regression models for both individual patterns and their combination and we find that motifs as features are better predictors of the future network organization than individual node centralities.
User-Directed Loop-Transformations in Clang

Directives for the compiler such as pragmas can help programmers to separate an algorithm's semantics from its optimization. This keeps the code understandable and easier to optimize for different platforms. Simple transformations such as loop unrolling are already implemented in most mainstream compilers. We recently submitted a proposal to add generalized loop transformations to the OpenMP standard. We are also working on an implementation in LLVM/Clang/Polly to show its feasibility and usefulness. The current prototype allows applying patterns common to matrix-matrix multiplication optimizations.
Computation of the maximum likelihood estimator in low-rank factor analysis

Factor analysis is a classical multivariate dimensionality reduction technique popularly used in statistics, econometrics and data science. Estimation for factor analysis is often carried out via the maximum likelihood principle, which seeks to maximize the Gaussian likelihood under the assumption that the positive definite covariance matrix can be decomposed as the sum of a low-rank positive semidefinite matrix and a diagonal matrix with nonnegative entries. This leads to a challenging rank constrained nonconvex optimization problem, for which very few reliable computational algorithms are available. We reformulate the low-rank maximum likelihood factor analysis task as a nonlinear nonsmooth semidefinite optimization problem, study various structural properties of this reformulation; and propose fast and scalable algorithms based on difference of convex optimization. Our approach has computational guarantees, gracefully scales to large problems, is applicable to situations where the sample covariance matrix is rank deficient and adapts to variants of the maximum likelihood problem with additional constraints on the model parameters. Our numerical experiments validate the usefulness of our approach over existing state-of-the-art approaches for maximum likelihood factor analysis.
A homology theory for a special family of semi-groups

In this paper, we construct a new homology theory for semi-groups satisfying the right self-distributivity axiom or the idempotency axiom. Next, we consider the geometric realization corresponding to the homology theory. We continue with the comparison of this homology theory with one-term and two-term (rack) homology theories of self-distributive algebraic structures. Finally, we propose connections between the homology theory and knot theory via Temperley–Lieb algebras.
Dynamics and scattering of a massless particle

Abstract We estimate the probability of a photon to move faster than light, under the Einstein dynamics which, unlike the wave equation or Maxwell wave dynamics, has singular dispersion relation at zero momentum. We show that this probability goes to zero with time, using propagation estimates suitably multi-scaled to control the contribution of low frequencies. We then prove minimal velocity bounds.
Exponentiation and Euler measure

Two of the pillars of combinatorics are the notion of choosing an arbitrary subset of a set with n elements (which can be done in 2 n ways), and the notion of choosing a k-element subset of a set with n elements (which can be done in $$ n \choose k $$ ways). In this article I sketch the beginnings of a theory that would import these notions into the category of hedral sets in the sense of Morelli and the category of polyhedral sets in the sense of Schanuel. Both of these theories can be viewed as extensions of the theory of finite sets and mappings between finite sets, with the concept of cardinality being replaced by the more general notion of Euler measure (sometimes called combinatorial Euler characteristic). I prove a “functoriality” theorem (Theorem 1) for subset-selection in the context of polyhedral sets, which provides quasi-combinatorial interpretations of assertions such as $$ 2^{-1} = {1 \over 2} \quad\mathrm{and}\quad{{1/2} \choose {2}} = -{1 \over 8} $$ . Furthermore, the operation of forming a power set can be viewed as a special case of the operation of forming the set of all mappings from one set to another; I conclude the article with a polyhedral analogue of the set of all mappings between two finite sets, and a restrictive but suggestive result (Theorem 2) that offers a hint of what a general theory of exponentiation in the polyhedral category might look like. (Other glimpses into the theory may be found in [11].)
Column selection via adaptive sampling

Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches.
Stable soliton resolution for exterior wave maps in all equivariance classes

Abstract In this paper we consider finite energy l-equivariant wave maps from R t , x 1 + 3 \ ( R × B ( 0 , 1 ) ) → S 3 with a Dirichlet boundary condition at r = 1 , and for all l ∈ N . Each such l-equivariant wave map has a fixed integer-valued topological degree, and in each degree class there is a unique harmonic map, which minimizes the energy for maps of the same degree. We prove that an arbitrary l-equivariant exterior wave map with finite energy scatters to the unique harmonic map in its degree class, i.e., soliton resolution. This extends the recent results of the first, second, and fourth authors on the 1-equivariant equation to higher equivariance classes, and thus completely resolves a conjecture of Bizon, Chmaj and Maliborski, who observed this asymptotic behavior numerically. The proof relies crucially on exterior energy estimates for the free radial wave equation in dimension d = 2 l + 3 , which are established in the companion paper [13] .
Understanding Recurrent Neural State Using Memory Signatures

We demonstrate a network visualization technique to analyze the recurrent state inside the LSTMs/GRUs used commonly in language and acoustic models. Interpreting intermediate state and network activations inside end-to-end models remains an open challenge. Our method allows users to understand exactly how much and what history is encoded inside recurrent state in grapheme sequence models. Our procedure trains multiple decoders that predict prior input history. Compiling results from these decoders, a user can obtain a signature of the recurrent kernel that characterizes its memory behavior. We demonstrate this method's usefulness in revealing information divergence in the bases of recurrent factorized kernels, visualizing the character-level differences between the memory of n-gram and recurrent language models, and extracting knowledge of history encoded in the layers of grapheme-based end-to-end ASR networks.
Refining the Two-Dimensional Signed Small Ball Inequality

The two-dimensional signed small ball inequality states that for all possible choices of signs, $$\begin{aligned} \left\| \sum _{|R| = 2^{-n}}{ \varepsilon _R h_R} \right\| _{L^{\infty }} \gtrsim n, \end{aligned}$$ where the summation runs over all dyadic rectangles in the unit square and $$h_R$$ denotes the associated Haar function. This inequality first appeared in the work of Talagrand, and alternative proofs are due to Temlyakov and Bilyk & Feldheim (who showed that the supremum equals $$n+1$$ in all cases). We prove a stronger result: for all integers $$0\le k \le n+1$$ , all possible choices of signs, and all dyadic rectangles Q with $$|Q| \ge 2^{-n-1}$$ , $$\begin{aligned} \left| \left\{ x \in Q: \sum _{|R| = 2^{-n}}{ \varepsilon _R h_R} = n + 1 - 2k\right\} \right| = \frac{|Q|}{2^{n+1}}\left( {\begin{array}{c}n+1\\ k\end{array}}\right) . \end{aligned}$$
Multi-Instance Visual-Semantic Embedding

Visual-semantic embedding models have been recently proposed and shown to be effective for image classification and zero-shot learning, by mapping images into a continuous semantic label space. Although several approaches have been proposed for single-label embedding tasks, handling images with multiple labels (which is a more general setting) still remains an open problem, mainly due to the complex underlying corresponding relationship between image and its labels. In this work, we present Multi-Instance visual-semantic Embedding model (MIE) for embedding images associated with either single or multiple labels. Our model discovers and maps semantically-meaningful image subregions to their corresponding labels. And we demonstrate the superiority of our method over the state-of-the-art on two tasks, including multi-label image annotation and zero-shot learning.
A games-in-games approach to mosaic command and control design of dynamic network-of-networks for secure and resilient multi-domain operations

This paper presents a games-in-games approach to provide design guidelines for mosaic command and control that enables the secure and resilient multi-domain operations. Under the mosaic design, pieces or agents in the network are equipped with flexible interoperability and the capability of self-adaptability, self-healing, and resiliency so that they can reconfigure their responses to achieve the global mission in spite of failures of nodes and links in the adversarial environment. The proposed games-in-games approach provides a system-of-systems science for mosaic distributed design of large-scale systems. Specifically, the framework integrates three layers of design for each agent including strategic layer, tactical layer, and mission layer. Each layer in the established model corresponds to a game of a different scale that enables the integration of threat models and achieve self-mitigation and resilience capabilities. The solution concept of the developed multi-layer multi-scale mosaic design is characterized by Gestalt Nash equilibrium (GNE) which considers the interactions between agents across different layers. The developed approach is applicable to modern battlefield networks which are composed of heterogeneous assets that access highly diverse and dynamic information sources over multiple domains. By leveraging mosaic design principles, we can achieve the desired operational goals of deployed networks in a case study and ensure connectivity among entities for the exchange of information to accomplish the mission.
Partition-based Feasible Integer Solution Pre-computation for Hybrid Model Predictive Control

For multiparametric mixed-integer convex programming problems such as those encountered in hybrid model predictive control, we propose an algorithm for generating a feasible partition of a subset of the parameter space. The result is a static map from the current parameter to a suboptimal integer solution such that the remaining convex program is feasible. Convergence is proved with a new insight that the overlap among the feasible parameter sets of each integer solution governs the partition complexity. The partition is stored as a tree which makes querying the feasible solution efficient. The algorithm can be used to warm start a mixed integer solver with a real-time guarantee or to provide a reference integer solution in several suboptimal MPC schemes. The algorithm is tested on randomly generated systems with up to six states, demonstrating the effectiveness of the approach.
Internet immunity and the freedom to code

The Internet’s freedom to code is in jeopardy. In 1996, Congress enacted 47 U.S.C. § 230, which says Internet services aren’t liable for third-party content in many cases. In practice, for over two decades, Section 230 has legally immunized coders’ decisions about how to gather, organize, and publish third party content. Section 230 has become a political target by all sides, but reforming it will impair coding freedom. This essay explains how Section 230 came into existence, the effects it has had, and why technologists should rally behind it to preserve their ability to build the next generation of Internet services.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal probabilistic prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.
Structured Local Optima in Sparse Blind Deconvolution

Blind deconvolution is a ubiquitous problem aiming to recover a convolution kernel $\boldsymbol a_{0}\in \mathbb R ^{k}$ and an activation signal $\boldsymbol x_{0}\in \mathbb R ^{m}$ from their convolution $\boldsymbol y\in \mathbb R ^{m}$ . Unfortunately, this is an ill-posed problem in general. This paper focuses on the short and sparse blind deconvolution problem, where the convolution kernel is short ( $k\ll m$ ) and the activation signal is sparsely and randomly supported ( $\left \|{ \boldsymbol x_{0} }\right \|_{0}\ll m$ ). This variant captures the structure of the convolutional signals in several important application scenarios. In this paper, we normalize the convolution kernel to have unit Frobenius norm and then cast the blind deconvolution problem as a nonconvex optimization problem over the kernel sphere. We demonstrate that (i) in a certain region of the sphere, every local optimum is close to some shift truncation of the ground truth, and (ii) for a generic unit kernel $\boldsymbol a_{0}$ , when the sparsity of activation signal satisfies $\theta \lesssim k^{-2/3}$ and number of measurements $m\gtrsim \mathop {\mathrm {poly}}\nolimits \left ({k }\right) $ , the proposed initialization method together with a descent algorithm which escapes strict saddle points recovers some shift truncation of the ground truth kernel.
The fitness cost of mismatch repair mutators in Saccharomyces cerevisiae: partitioning the mutational load.

Mutational load is the depression in a population’s mean fitness that results from the continual influx of deleterious mutations. Here, we directly estimate the mutational load in a population of haploid Saccharomyces cerevisiae that are deficient for mismatch repair. We partition the load in haploids into two components. To estimate the load due to nonlethal mutations, we measure the competitive fitness of hundreds of randomly selected clones from both mismatch-repair-deficient and -proficient populations. Computation of the mean clone fitness for the mismatch-repair-deficient strain permits an estimation of the nonlethal load, and the histogram of fitness provides an interesting visualization of a loaded population. In a separate experiment, in order to estimate the load due to lethal mutations (i.e. the lethal mutation rate), we manipulate thousands of individual pairs of mother and daughter cells and track their fates. These two approaches yield point estimates for the two contributors to load, and the addition of these estimates is nearly equal to the separately measured short-term competitive fitness deficit for the mismatch-repair-deficient strain. This correspondence suggests that there is no need to invoke direct fitness effects to explain the fitness difference between mismatch-repair-deficient and -proficient strains. Assays in diploids are consistent with deleterious mutations in diploids tending towards recessivity. These results enhance our understanding of mutational load, a central population genetics concept, and we discuss their implications for the evolution of mutation rates.
BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer.

An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets.
Homogenization of a class of one-dimensional nonconvex viscous Hamilton-Jacobi equations with random potential

AbstractWe prove the homogenization of a class of one-dimensional viscous Hamilton-Jacobi equations with random Hamiltonians that are nonconvex in the gradient variable. Due to the special form of ...
Simulating Markov Random Fields With a Conclique-Based Gibbs Sampler

For spatial and network data, we consider models formed from a Markov random field (MRF) structure and the specification of a conditional distribution for each observation. Fast simulation from suc...
A numerical method for an inverse source problem for parabolic equations and its application to a coefficient inverse problem

Two main aims of this paper are to develop a numerical method to solve an inverse source problem for parabolic equations and apply it to solve a nonlinear coefficient inverse problem. The inverse source problem in this paper is the problem to reconstruct a source term from external observations. Our method to solve this inverse source problem consists of two stages. We first establish an equation of the derivative of the solution to the parabolic equation with respect to the time variable. Then, in the second stage, we solve this equation by the quasi-reversibility method. The inverse source problem considered in this paper is the linearization of a nonlinear coefficient inverse problem. Hence, iteratively solving the inverse source problem provides the numerical solution to that coefficient inverse problem. Numerical results for the inverse source problem under consideration and the corresponding nonlinear coefficient inverse problem are presented.
Recovirus NS1-2 Has Viroporin Activity That Induces Aberrant Cellular Calcium Signaling To Facilitate Virus Replication

Enteric viruses in the Caliciviridae family cause acute gastroenteritis in humans and animals, but the cellular processes needed for virus replication and disease remain unknown. A common strategy among enteric viruses, including rotaviruses and enteroviruses, is to encode a viral ion channel (i.e., viroporin) that is targeted to the endoplasmic reticulum (ER) and disrupts host calcium (Ca2+) homeostasis. Previous reports have demonstrated genetic and functional similarities between the nonstructural proteins of caliciviruses and enteroviruses, including the calicivirus NS1-2 protein and the 2B viroporin of enteroviruses. However, it is unknown whether caliciviruses alter Ca2+ homeostasis for virus replication or whether the NS1-2 protein has viroporin activity like its enterovirus counterpart. To address these questions, we used Tulane virus (TV), a rhesus enteric calicivirus, to examine Ca2+ signaling during infection and determine whether NS1-2 has viroporin activity that disrupts Ca2+ homeostasis. We found that TV increases Ca2+ signaling during infection and that increased cytoplasmic Ca2+ levels are important for efficient replication. Further, TV NS1-2 localizes to the endoplasmic reticulum, the predominant intracellular Ca2+ store, and the NS2 region has characteristics of a viroporin domain (VPD). NS1-2 had viroporin activity in a classic bacterial functional assay and caused aberrant Ca2+ signaling when expressed in mammalian cells, but truncation of the VPD abrogated these activities. Together, our data provide new mechanistic insights into the function of the NS2 region of NS1-2 and support the premise that enteric viruses, including those within Caliciviridae, exploit host Ca2+ signaling to facilitate their replication.IMPORTANCE Tulane virus is one of many enteric caliciviruses that cause acute gastroenteritis and diarrheal disease. Globally, enteric caliciviruses affect both humans and animals and amass >65 billion dollars per year in treatment and health care-associated costs, thus imposing an enormous economic burden. Recent progress has resulted in several cultivation systems (B cells, enteroids, and zebrafish larvae) to study human noroviruses, but mechanistic insights into the viral factors and host pathways important for enteric calicivirus replication and infection are still largely lacking. Here, we used Tulane virus, a calicivirus that is biologically similar to human noroviruses and can be cultivated by conventional cell culture, to identify and functionally validate NS1-2 as an enteric calicivirus viroporin. Viroporin-mediated calcium signaling may be a broadly utilized pathway for enteric virus replication, and its existence within caliciviruses provides a novel approach to developing antivirals and comprehensive therapeutics for enteric calicivirus diarrheal disease outbreaks.
Gaussian Conditionally Markov Sequences: Singular/Nonsingular

Most existing results about modeling and characterizing Gaussian Markov, reciprocal, and conditionally Markov (CM) processes assume nonsingularity of the processes. This assumption makes the analysis easier, but restricts application of these processes. This paper studies, models, and characterizes the general (singular/nonsingular) Gaussian CM (including reciprocal and Markov) sequence. For example, to our knowledge, there is no dynamic model for the general (singular/nonsingular) Gaussian reciprocal sequence in the literature. We obtain two such models from the CM viewpoint. As a result, the significance of studying reciprocal sequences from the CM viewpoint is demonstrated. The results of this paper unify singular and nonsingular Gaussian CM (including reciprocal and Markov) sequences and provide tools for their application. An application of CM sequences in trajectory modeling with a destination is discussed, and illustrative examples are presented.
Regulated spindle orientation buffers tissue growth in the epidermis

Tissue homeostasis requires a balance between progenitor cell proliferation and loss. Mechanisms that maintain this robust balance are needed to avoid tissue loss or overgrowth. Here we demonstrate that regulation of spindle orientation/asymmetric cell divisions is one mechanism that is used to buffer changes in proliferation and tissue turnover in mammalian skin. Genetic and pharmacologic experiments demonstrate that asymmetric cell divisions were increased in hyperproliferative conditions and decreased under hypoproliferative conditions. Further, active K-Ras also increased the frequency of asymmetric cell divisions. Disruption of spindle orientation in combination with constitutively active K-Ras resulted in massive tissue overgrowth. Together, these data highlight the essential roles of spindle orientation in buffering tissue homeostasis in response to perturbations.
Fast and unbiased purification of RNA-protein complexes after UV cross-linking

Abstract Post-transcriptional regulation of gene expression in cells is facilitated by formation of RNA-protein complexes (RNPs). While many methods to study eukaryotic (m)RNPs rely on purification of polyadenylated RNA, other important regulatory RNA classes or bacterial mRNA could not be investigated at the same depth. To overcome this limitation, we developed Phenol Toluol extraction (PTex), a novel and unbiased method for the purification of UV cross-linked RNPs in living cells. PTex is a fast (2–3 h) and simple protocol. The purification principle is solely based on physicochemical properties of cross-linked RNPs, enabling us to interrogate RNA-protein interactions system-wide and beyond poly(A) RNA from a variety of species and source material. Here, we are presenting an introduction of the underlying separation principles and give a detailed discussion of the individual steps as well as incorporation of PTex in high-throughput pipelines.
When low beats high: Riding the sales seasonality premium

Abstract This paper examines whether predictable seasonal patterns in firm fundamentals generate time variation in stock returns. Our findings indicate that stock returns are counterseasonal. Specifically, a long-short strategy of buying low-sales season stocks and shorting high-sales season stocks produces an annual alpha of 8.4% (14.5% over the last decade). This seasonal effect has a relatively high Sharpe ratio and occurs independently of previously documented seasonal anomalies. We analyze several possible hypotheses for this phenomenon.
Pseudo likelihood estimation and iterative proportional refitting in network tomography

Summary form only given. The network origin-destination (OD) matrix is very important for network performance improvement. In this talk, we review the pseudo likelihood approach (Liang and Yu, IEEE Trans. Signal Processing, to appear) for OD matrix estimation based on link counts collected at routers. The basic idea of pseudo likelihood is to construct simple subproblems and ignore the dependences among the subproblems to form a product likelihood of the subproblems. In doing so, we balance the computational requirements and estimation accuracies. As in Cao, Davis, Vander Wiel and Yu (J. Amer. Statist. Assoc, 2000), iterative proportional fitting (IPF) is used in our approach to match initial estimates obtained from pseudo likelihood estimation with the linear constraints imposed by observed link counts. We demonstrate the relationship between IPF and entropy minimization, and give the convergence rate of the IPF algorithm. Last, we present the connections and differences between IPF and the entropy penalization based method proposed by Donoho, Lund, Roughan and Zhang (Tech. Report 2003-15, Statistics Department, Stanford Univ).
Dissecting the dynamics of signaling events in the BMP, WNT, and NODAL cascade during self-organized fate patterning in human gastruloids.

During gastrulation, the pluripotent epiblast self-organizes into the 3 germ layers—endoderm, mesoderm and ectoderm, which eventually form the entire embryo. Decades of research in the mouse embryo have revealed that a signaling cascade involving the Bone Morphogenic Protein (BMP), WNT, and NODAL pathways is necessary for gastrulation. In vivo, WNT and NODAL ligands are expressed near the site of gastrulation in the posterior of the embryo, and knockout of these ligands leads to a failure to gastrulate. These data have led to the prevailing view that a signaling gradient in WNT and NODAL underlies patterning during gastrulation; however, the activities of these pathways in space and time have never been directly observed. In this study, we quantify BMP, WNT, and NODAL signaling dynamics in an in vitro model of human gastrulation. Our data suggest that BMP signaling initiates waves of WNT and NODAL signaling activity that move toward the colony center at a constant rate. Using a simple mathematical model, we show that this wave-like behavior is inconsistent with a reaction-diffusion–based Turing system, indicating that there is no stable signaling gradient of WNT/NODAL. Instead, the final signaling state is homogeneous, and spatial differences arise only from boundary effects. We further show that the durations of WNT and NODAL signaling control mesoderm differentiation, while the duration of BMP signaling controls differentiation of CDX2-positive extra-embryonic cells. The identity of these extra-embryonic cells has been controversial, and we use RNA sequencing (RNA-seq) to obtain their transcriptomes and show that they closely resemble human trophoblast cells in vivo. The domain of BMP signaling is identical to the domain of differentiation of these trophoblast-like cells; however, neither WNT nor NODAL forms a spatial pattern that maps directly to the mesodermal region, suggesting that mesoderm differentiation is controlled dynamically by the combinatorial effect of multiple signals. We synthesize our data into a mathematical model that accurately recapitulates signaling dynamics and predicts cell fate patterning upon chemical and physical perturbations. Taken together, our study shows that the dynamics of signaling events in the BMP, WNT, and NODAL cascade in the absence of a stable signaling gradient control fate patterning of human gastruloids.
Dynamic wake modulation induced by utility-scale wind turbine operation

Abstract Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly changing, the rate of wake recovery can be significantly influenced by dynamic wake modulation, which has not yet been explored. Here we present the first investigation of dynamic wake modulation in the near wake of an operational utility-scale wind turbine, and quantify its relationship with changing conditions. This experimental investigation is enabled using novel super-large-scale flow visualization with natural snowfall, providing unprecedented spatiotemporal resolution to resolve instantaneous changes of the wake envelope in the field. These measurements reveal the significant influence of dynamic wake modulation, which causes an increase in flux across the wake boundary of 11% on average, on wake recovery, providing insights into necessary modifications to traditional wake and farm models. Further, our study uncovers the direct connection between dynamic wake modulation and operational parameters readily available to the turbine controller such as yaw error, blade pitch, and tip speed ratio. These connections pave the way for more precise wake prediction and control algorithms under field conditions for wind farm optimization.
Weakly-Supervised Action Localization With Background Modeling

We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground and background frames who's appearance is explicitly modeled. Most prior work ignores the background, but we show that modeling it allows our system to learn a richer notions of actions and their temporal extents. Second, we combine bottom-up, class-agnostic attention modules with top-down, class-specific activation maps, using the latter as form of self-supervision for the former. Doing so allows our model to learn a more accurate model of attention without explicit temporal supervision. These modifications lead to $~10\%$ $AP@IoU$=0.5 improvement over existing systems on THUMOS14. Our proposed weakly-supervised system outperforms the recent state-of-the-art by at least $4.3\%$ $AP@IoU$=0.5. Finally, we demonstrate that weakly-supervised learning can be used to aggressively scale-up learning to in-the-wild, uncurated Instagram videos (where relevant frames and videos are automatically selected through attentional processing). This allows our weakly supervised approach to even outperform fully-supervised methods for action detection at some overlap thresholds.
Design and development of energy efficient re-roofing solutions

Abstract Although the study of roofing systems (from coverings to structure of the roof) is of great significance to energy efficiency and sustainable development, design and construction of new roofs on top of existing roofs in buildings has not received sufficient attention. This study discusses re-roofing as one of the key options to reduce energy consumption and improve overall building performance. The more conventional choice and more frequently chosen option for building energy retrofit is vertical building envelope components, however, this paper presents the case study of a proposed retrofitting concept for the roof of an existing building. Using a simulation methodology, the research demonstrates how the proposed retrofitting concept improves the energy performance using electricity generated and external conduction gain as evaluation metrics. The proposed novel re-roofing concept consists of thermal insulation, waterproofing and electric energy generation properties. More specifically, the design concept includes a three-layer roofing system consisting of (from exterior toward interior): a PV panel, an EPDM membrane and an insulation layer. The main goal of this study is to develop a concept for an innovative re-roofing solution that demonstrates the feasibility of turning an old building into a watertight and energy producing system.
Eyebrow and Eyelid Dimensions: An Anthropometric Analysis of African Americans and Caucasians

Background:Knowledge of the average dimensions of periocular features based on age, gender, and race is critical for surgeons so that complications can be avoided and ideal outcomes can be achieved. In this study, the authors sought to determine whether significant differences exist for certain eyel
Combined Targeting of Mutant p53 and Jumonji Family Histone Demethylase Augments Therapeutic Efficacy of Radiation in H3K27M DIPG.

Diffuse intrinsic pontine glioma (DIPG) is an aggressive pediatric brainstem tumor with a 5-year survival of 70-80% of H3K27M DIPG, and mutant p53 status is associated with a decreased response to radiation treatment and worse overall prognosis. Recent evidence indicates that H3K27M mutation disrupts tri-methylation at H3K27 leading to aberrant gene expression. Jumonji family histone demethylases collaborates with H3K27 mutation in DIPG by erasing H3K27 trimethylation and thus contributing to derepression of genes involved in tumorigenesis. Since the first line of treatment for pediatric DIPG is fractionated radiation, we investigated the effects of Jumonji demethylase inhibition with GSK-J4, and mutant p53 targeting/oxidative stress induction with APR-246, on radio-sensitization of human H3K27M DIPG cells. Both APR-246 and GSK-J4 displayed growth inhibitory effects as single agents in H3K27M DIPG cells. Furthermore, both of these agents elicited mild radiosensitizing effects in human DIPG cells (sensitizer enhancement ratios (SERs) of 1.12 and 1.35, respectively; p < 0.05). Strikingly, a combination of APR-246 and GSK-J4 displayed a significant enhancement of radiosensitization, with SER of 1.50 (p < 0.05) at sub-micro-molar concentrations of the drugs (0.5 μM). The molecular mechanism of the observed radiosensitization appears to involve DNA damage repair deficiency triggered by APR-246/GSK-J4, leading to the induction of apoptotic cell death. Thus, a therapeutic approach of combined targeting of mutant p53, oxidative stress induction, and Jumonji demethylase inhibition with radiation in DIPG warrants further investigation.
Una contienda acotada: la defensa transnacional de los derechos laborales de las mujeres en las maquiladoras de México

Transnational advocacy on the rights of women workers in Mexico?s maquiladoras was central to broader international debates on human rights in the 1990s. Thi...
Gradients as Features for Deep Representation Learning

We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient features and the activation of the network. We show that our model provides a local linear approximation to a underlying deep model, and discuss important theoretical insight. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradients. Our method is evaluated across a number of representation learning tasks on several datasets and using different network architectures. We demonstrate strong results in all settings. And our results are well-aligned with our theoretical insight.
Analyzing Controllability of Bilinear Systems on Symmetric Groups: Mapping Lie Brackets to Permutations

Bilinear systems emerge in a wide variety of fields as natural models for dynamical systems ranging from robotics to quantum dots. Analyzing controllability of such systems is of fundamental and practical importance, for example, for the design of optimal control laws, stabilization of unstable systems, and minimal realization of input–output relations. Tools from the Lie theory have been adopted to establish controllability conditions for bilinear systems, and the most notable development was the Lie algebra rank condition (LARC). However, the application of the LARC may be computationally expensive for high-dimensional systems. In this article, we present an alternative and effective algebraic approach to investigate controllability of bilinear systems. The central idea is to map Lie bracket operations of the vector fields governing the system dynamics to permutation multiplications on a symmetric group, so that controllability and the controllable submanifold can be characterized by permutation orbits. The method is further shown to be applicable to characterize controllability of systems defined on undirected graphs, such as multiagent systems with controlled couplings between agents and Markov chains with tunable transition rates between states, which, in turn, reveals a graph representation of controllability through graph connectivity.
Adversarial Transformations for Semi-Supervised Learning

We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. RAT is designed to enhance robustness of the output distribution of class prediction for a given data against input perturbation. RAT is an extension of Virtual Adversarial Training (VAT) in such a way that RAT adversraialy transforms data along the underlying data distribution by a rich set of data transformation functions that leave class label invariant, whereas VAT simply produces adversarial additive noises. In addition, we verified that a technique of gradually increasing of perturbation region further improves the robustness. In experiments, we show that RAT significantly improves classification performance on CIFAR-10 and SVHN compared to existing regularization methods under standard semi-supervised image classification settings.
Model catalysts: reductionism for understanding

A model heterogeneous catalyst is synthesized for the purpose of understanding how it works. The model is inspired by a real catalyst. The latter is made simpler with a known structure and texture, with identifiable and countable sites. The goal is to approach the ideal molecular catalyst with identical and non‐interacting active sites, i.e., a single site catalyst. Five examples of model catalysts are discussed with the understanding gained from their study.
Bayesian spatially varying coefficient models in the spBayes R package

Abstract This paper describes and illustrates new functionality for fitting spatially varying coefficients models in the spBayes (version 0.4–2) R package. The new spSVC function uses a computationally efficient Markov chain Monte Carlo algorithm and extends current spBayes functions, that fit only space-varying intercept regression models, to fit independent or multivariate Gaussian process random effects for any set of columns in the regression design matrix. Newly added OpenMP parallelization options for spSVC are discussed and illustrated, as well as helper functions for joint and point-wise prediction and model fit diagnostics. The utility of the proposed models is illustrated using a PM10 analysis over central Europe.
Extending Policy from One-Shot Learning through Coaching

Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations, often followed by episodes of coaching that tune and refine the execution during practice. Adopting a similar framework for teaching robots through demonstrations makes teaching tasks highly intuitive and imitating the refinement of complex tasks through coaching improves the efficacy. Unlike traditional Learning from Demonstration (LfD) approaches which rely on multiple demonstrations to train a task, we present a novel one-shot learning from demonstration approach, augmented by coaching, to transfer the task from task expert to robot. The demonstration is automatically segmented into a sequence of a priori skills (the task policy) parametrized to match task goals. During practice, the robotic skills self-evaluate their performances and refine the task policy to locally optimize cumulative performance. Then, human coaching further refines the task policy to explore and globally optimize the net performance. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. The proposed approach is evaluated using the task of scooping and unscooping granular media. The self-evaluator of the scooping skill uses the realtime force signature and resistive force theory to minimize scooping resistance similar to how humans scoop. Coaching feedback focuses modifications to sub-domains of the action space, using RL to converge to desired performance. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching achieving a success rate of ≈90%.