cssi/SciSciGPT-SciSciNet huggingface dataset:
Hodge theory in combinatorics

George Birkhoff proved in 1912 that the number of proper colorings of a finite graph G with n colors is a polynomial in n, called the chromatic polynomial of G. Read conjectured in 1968 that for any graph G, the sequence of absolute values of coefficients of the chromatic polynomial is unimodal: it goes up, hits a peak, and then goes down. Read's conjecture was proved by June Huh in a 2012 paper making heavy use of methods from algebraic geometry. Huh's result was subsequently refined and generalized by Huh and Katz, again using substantial doses of algebraic geometry. Both papers in fact establish log-concavity of the coefficients, which is stronger than unimodality. The breakthroughs of Huh and Huh-Katz left open the more general Rota-Welsh conjecture where graphs are generalized to (not necessarily representable) matroids and the chromatic polynomial of a graph is replaced by the characteristic polynomial of a matroid. The Huh and Huh-Katz techniques are not applicable in this level of generality, since there is no underlying algebraic geometry to which to relate the problem. But in 2015 Adiprasito, Huh, and Katz announced a proof of the Rota-Welsh conjecture based on a novel approach motivated by but not making use of any results from algebraic geometry. The authors first prove that the Rota-Welsh conjecture would follow from combinatorial analogues of the Hard Lefschetz Theorem and Hodge-Riemann relations in algebraic geometry. They then implement an elaborate inductive procedure to prove the combinatorial Hard Lefschetz Theorem and Hodge-Riemann relations using purely combinatorial arguments. We will survey these developments.
Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing

Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and speech recognition. Ideally, systems would employ near-sensor computation to execute these tasks at sensor endpoints to maximize data reduction and minimize data movement. However, near-sensor computing presents its own set of challenges such as operating power constraints, energy budgets, and communication bandwidth capacities. In this paper, we propose a stochastic-binary hybrid design which splits the computation between the stochastic and binary domains for near-sensor NN applications. In addition, our design uses a new stochastic adder and multiplier that are significantly more accurate than existing adders and multipliers. We also show that retraining the binary portion of the NN computation can compensate for precision losses introduced by shorter stochastic bit-streams, allowing faster run times at minimal accuracy losses. Our evaluation shows that our hybrid stochastic-binary design can achieve 9.8x energy efficiency savings, and application-level accuracies within 0.05% compared to conventional all-binary designs.
Sample Complexity of Automated Mechanism Design

The design of revenue-maximizing combinatorial auctions, i.e. multi item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, automated mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable automated mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class. In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in automated mechanism design. In particular, we provide tight sample complexity bounds on the number of samples needed to guarantee that the empirical revenue of the designed mechanism on the samples is close to its expected revenue on the underlying, unknown distribution over bidder valuations, for each of the auction classes in the hierarchy. In addition to helping set automated mechanism design on firm foundations, our results also push the boundaries of learning theory. In particular, the hypothesis functions used in our contexts are defined through multi stage combinatorial optimization procedures, rather than simple decision boundaries, as are common in machine learning.
Twists of quantum Borel algebras

Abstract We classify Drinfeld twists for the quantum Borel subalgebra u q ( b ) in the Frobenius–Lusztig kernel u q ( g ) , where g is a simple Lie algebra over C and q an odd root of unity. More specifically, we show that alternating forms on the character group of the group of grouplikes for u q ( b ) generate all twists for u q ( b ) , under a certain algebraic group action. This implies a simple classification of finite-dimensional Hopf algebras whose categories of representations are tensor equivalent to that of u q ( b ) . We also show that cocycle twists for the corresponding De Concini–Kac algebra are in bijection with alternating forms on the aforementioned character group.
F -purity of hypersurfaces

In this note, we study F-purity of pairs, and show (as is the case with log canonicity) that F-purity is preserved at the F-pure threshold. We also characterize when F-purity is equivalent to sharp F-purity, an alternate notion of purity for pairs introduced in [Sch08]. These results on purity at the threshold generalize results appearing in [Har06, Sch08], and were expected to hold by many experts in the field. We conclude by extending results in [BMS09] on the set of all F-pure thresholds to the most general setting.
On the Growth of a Superlinear Preferential Attachment Scheme

We consider an evolving preferential attachment random graph model where at discrete times a new node is attached to an old node, selected with probability proportional to a superlinear function of its degree. For such schemes, it is known that the graph evolution condenses, that is a.s. in the limit graph there will be a single random node with infinite degree, while all others have finite degree. In this note, we establish a.s. law of large numbers type limits and fluctuation results, as \(n\uparrow \infty \), for the counts of the number of nodes with degree \(k\ge 1\) at time \(n\ge 1\). These limits rigorously verify and extend a physical picture of Krapivisky et al. (Phys Rev Lett 85:4629–4632, 2000 [16]) on how the condensation arises with respect to the degree distribution.
Which distribution distances are sublinearly testable

Given samples from an unknown distribution p and a description of a distribution q, are p and q close or far? This question of "identity testing" has received significant attention in the case of testing whether p and q are equal or far in total variation distance. However, in recent work [VV11a, ADK15, DP17], the following questions have been been critical to solving problems at the frontiers of distribution testing: • Alternative Distances: Can we test whether p and q are far in other distances, say Hellinger? • Tolerance: Can we test when p and q are close, rather than equal? And if so, close in which distances? Motivated by these questions, we characterize the complexity of distribution testing under a variety of distances, including total variation, e2, Hellinger, Kullback-Leibler, and χ2. For each pair of distances d1 and d2, we study the complexity of testing if p and q are close in d1 versus far in d2, with a focus on identifying which problems allow strongly sublinear testers (i.e., those with complexity O(n1−γ) for some γ > 0 where n is the size of the support of the distributions p and q). We provide matching upper and lower bounds for each case. We also study these questions in the case where we only have samples from q (equivalence testing), showing qualitative differences from identity testing in terms of when tolerance can be achieved. Our algorithms fall into the classical paradigm of χ2-statistics, but require crucial changes to handle the challenges introduced by each distance we consider. Finally, we survey other recent results in an attempt to serve as a reference for the complexity of various distribution testing problems.
Cambrian frameworks for cluster algebras of affine type

We give a combinatorial model for the exchange graph and g-vector fan associated to any acyclic exchange matrix B of affine type. More specifically, we construct a reflection framework for B in the sense of [N. Reading and D. E. Speyer, "Combinatorial frameworks for cluster algebras"] and establish good properties of this framework. The framework (and in particular the g-vector fan) is constructed by combining a copy of the Cambrian fan for B with an antipodal copy of the Cambrian fan for -B.
Efficient Segmental Cascades for Speech Recognition.

Discriminative segmental models offer a way to incorporate flexible feature functions into speech recognition. However, their appeal has been limited by their computational requirements, due to the large number of possible segments to consider. Multi-pass cascades of segmental models introduce features of increasing complexity in different passes, where in each pass a segmental model rescores lattices produced by a previous (simpler) segmental model. In this paper, we explore several ways of making segmental cascades efficient and practical: reducing the feature set in the first pass, frame subsampling, and various pruning approaches. In experiments on phonetic recognition, we find that with a combination of such techniques, it is possible to maintain competitive performance while greatly reducing decoding, pruning, and training time.
Ulrich sheaves and higher-rank Brill–Noether theory

Abstract We show that the existence of an Ulrich sheaf on a projective variety X ⊆ P N is equivalent to the solution of a (possibly) higher-rank Brill–Noether problem for a curve on X that is rarely general in moduli. In addition, we exhibit a large family of curves for which this Brill–Noether problem admits a solution, and we show that existence of an Ulrich sheaf for a finite morphism of smooth projective varieties of any dimension implies sharp numerical constraints involving the degree of the map and the ramification divisor.
Quantized Matrix Completion for Personalized Learning.

The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e.g., correct/incorrect) graded learner responses to questions. The underlying factors are termed “concepts” (or knowledge components) and are used for learning analytics (LA), the estimation of learner concept-knowledge profiles, and for content analytics (CA), the estimation of question–concept associations and question difficulties. While SPARFA is a powerful tool for LA and CA, it requires a number of algorithm parameters (including the number of concepts), which are difficult to determine in practice. In this paper, we propose SPARFA-Lite, a convex optimization-based method for LA that builds on matrix completion, which only requires a single algorithm parameter and enables us to automatically identify the required number of concepts. Using a variety of educational datasets, we demonstrate that SPARFALite (i) achieves comparable performance in predicting unobserved learner responses to existing methods, including item response theory (IRT) and SPARFA, and (ii) is computationally more efficient.
A central limit theorem for the determinant of a Wigner matrix

Abstract We establish a central limit theorem for the log-determinant log | det ( M n ) | of a Wigner matrix M n , under the assumption of four matching moments with either the GUE or GOE ensemble. More specifically, we show that this log-determinant is asymptotically distributed like N ( log n ! − 1 2 log n , 1 2 log n ) R when one matches moments with GUE, and N ( log n ! − 1 4 log n , 1 4 log n ) R when one matches moments with GOE.
The Algebra of Conformal Blocks

For each simply connected, simple complex group $G$ we show that the direct sum of all vector bundles of conformal blocks on the moduli stack $\bar{\mathcal{M}}_{g, n}$ of stable marked curves carries the structure of a flat sheaf of commutative algebras. The fiber of this sheaf over a smooth marked curve $(C, \vec{p})$ agrees with the Cox ring of the moduli of quasi-parabolic principal $G-$bundles on $(C, \vec{p})$. We use the factorization rules on conformal blocks to produce flat degenerations of these algebras. These degenerations are toric in the case $G = SL_2(\mathbb{C}),$ and the resulting toric varieties are shown to be isomorphic to phylogenetic algebraic varieties from mathematical biology. We conclude with a proof that the Cox ring of the moduli stack of qausi-parabolic $SL_2(\mathbb{C})$ principal bundles over a generic curve is generated by conformal blocks of levels 1 and 2 with relations generated in degrees $2, 3,$ and 4.
Controllability Gramian spectra of random networks

We propose a theoretical framework to study the eigenvalue spectra of the controllability Gramian of systems with random state matrices, such as networked systems with a random graph structure. Using random matrix theory, we provide expressions for the moments of the eigenvalue distribution of the controllability Gramian. These moments can then be used to derive useful properties of the eigenvalue distribution of the Gramian (in some cases, even closed-form expressions for the distribution). We illustrate this framework by considering system matrices derived from common random graph and matrix ensembles, such as the Wigner ensemble, the Gaussian Orthogonal Ensemble (GOE), and random regular graphs. Subsequently, we illustrate how the eigenvalue distribution of the Gramian can be used to draw conclusions about the energy required to control random system.
Tait colorings, and an instanton homology for webs and foams

We use SO(3) gauge theory to dene a functor from a category of unoriented webs and foams to the category of nite-dimensional vector spaces over the eld of two elements. We prove a non-vanishing theorem for this SO(3) instanton homology of webs, using Gabai’s sutured manifold theory. It is hoped that the non-vanishing theorem may support a program to provide a new proof of the four-color theorem. e work of the rst author was supported by the National Science Foundation through NSF grants DMS-0904589 and DMS-1405652. e work of the second author was supported by NSF grants DMS-0805841 and DMS-1406348.
High-field carrier velocity and current saturation in graphene field-effect transistors

We obtain the output characteristics of graphene field-effect transistors by using the charge-control model for the current, based on the solution of the Boltzmann equation in the field-dependent relaxation time approximation. Closed expressions for the conductance, transconductance and saturation voltage are derived. We found good agreement with the experimental data of Meric et al. [1], without assuming a carrier density-dependent velocity saturation.
Robust Dialog State Tracking for Large Ontologies

The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level. This article describes a novel dialog state tracking method designed to work robustly under these conditions, using elaborate string matching, coreference resolution tailored for dialogs and a few other improvements. The method can correctly identify many values that are not explicitly present in the utterance. On the final evaluation, our method came in first among 7 competing teams and 24 entries. The F1-score achieved by our method was 9 and 7 percentage points higher than that of the runner-up for the utterance-level evaluation and for the subdialog-level evaluation, respectively.
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on manyclassical machine learning problems. However, recent studies have shown thatdeep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force adeep neural network (DNN) to provide adversary-selected outputs. Such attackscan seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles canbe crashed, illicit or illegal content can bypass content filters, or biometricauthentication systems can be manipulated to allow improper access. In thiswork, we introduce a defensive mechanism called defensive distillationto reduce the effectiveness of adversarial samples on DNNs. We analyticallyinvestigate the generalizability and robustness properties granted by the useof defensive distillation when training DNNs. We also empirically study theeffectiveness of our defense mechanisms on two DNNs placed in adversarialsettings. The study shows that defensive distillation can reduce effectivenessof sample creation from 95% to less than 0.5% on a studied DNN. Such dramaticgains can be explained by the fact that distillation leads gradients used inadversarial sample creation to be reduced by a factor of 1030. We alsofind that distillation increases the average minimum number of features thatneed to be modified to create adversarial samples by about 800% on one of theDNNs we tested.
AEOLUS: A MARKOV CHAIN MONTE CARLO CODE FOR MAPPING ULTRACOOL ATMOSPHERES. AN APPLICATION ON JUPITER AND BROWN DWARF HST LIGHT CURVES

Deducing the cloud cover and its temporal evolution from the observed planetary spectra and phase curves can give us major insight into the atmospheric dynamics. In this paper, we present Aeolus, a Markov chain Monte Carlo code that maps the structure of brown dwarf and other ultracool atmospheres. We validated Aeolus on a set of unique Jupiter Hubble Space Telescope (HST) light curves. Aeolus accurately retrieves the properties of the major features of the Jovian atmosphere, such as the Great Red Spot and a major 5 μm hot spot. Aeolus is the first mapping code validated on actual observations of a giant planet over a full rotational period. For this study, we applied Aeolus to J- and H-band HST light curves of 2MASS J21392676+0220226 and 2MASS J0136565+093347. Aeolus retrieves three spots at the top of the atmosphere (per observational wavelength) of these two brown dwarfs, with a surface coverage of 21% ± 3% and 20.3% ± 1.5%, respectively. The Jupiter HST light curves will be publicly available via ADS/VIZIR.
Finding conjugate stabilizer subgroups in PSL(2; q) and related groups

We reduce a case of the hidden subgroup problem (HSP) in SL(2; q), PSL(2; q), andPGL(2; q), three related families of finite groups of Lie type, to efficiently solvable HSPsin the affine group AGL(1; q). These groups act on projective space in an "almost"3-transitive way, and we use this fact in each group to distinguish conjugates of itsBorel (upper triangular) subgroup, which is also the stabilizer subgroup of an elementof projective space. Our observation is mainly group-theoretic, and as such breaks littlenew ground in quantum algorithms. Nonetheless, these appear to be the first positiveresults on the HSP in finite simple groups such as PSL(2; q).
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data.

Abstract Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning models often seek to optimize predictions by incorporating features that are not present in the databases readily available to providers and policy makers, limiting generalizability and implementation. Here we tested a number of machine learning classifiers for prediction of six-month mortality in a population of elderly Medicare beneficiaries, using an administrative claims database of the kind available to the majority of health care payers and providers. We show that machine learning classifiers substantially outperform current widely-used methods of risk prediction-but only when used with an improved feature set incorporating insights from clinical medicine, developed for this study. Our work has applications to supporting patient and provider decision making at the end of life, as well as population health-oriented efforts to identify patients at high risk of poor outcomes.
Lagrangian fillings of Legendrian 4-plat knots

We characterize which Legendrian 4-plat knots in the standard contact 3-space have exact orientable Lagrangian fillings. As a corollary, we show that the underlying smooth knot types of fillable Legendrian 4-plats are positive.
Generic vanishing and classification of irregular surfaces in positive characteristics

We establish a generic vanishing theorem for surfaces in characteristic $p$ that lift to $W_2(k)$ and use it for surface classification of surfaces of general type with Euler characteristic 1 and large Albanese dimension.
The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter

Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of socialmedia content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on noncontrolled data.
Approximating Bin Packing within O(log OPT * Log Log OPT) Bins

For bin packing, the input consists of n items with sizes between 0 and 1, which have to be assigned to a minimum number of bins of size 1. The seminal Karmarkar-Karp algorithm from '82 produces a solution with at most OPT + O(log2 OPT) bins. We provide the first improvement in now 3 decades and show that one can find a solution of cost OPT + O(log OPT * log log OPT) in polynomial time. This is achieved by rounding a fractional solution to the Gilmore-Gomory LP relaxation using the Entropy Method from discrepancy theory. The result is constructive via algorithms of Bansal and Lovett-Meka.
Bridge trisections of knotted surfaces in 4-manifolds.

We prove that every smoothly embedded surface in a 4-manifold can be isotoped to be in bridge position with respect to a given trisection of the ambient 4-manifold; that is, after isotopy, the surface meets components of the trisection in trivial disks or arcs. Such a decomposition, which we call a generalized bridge trisection, extends the authors’ definition of bridge trisections for surfaces in S 4 . Using this construction, we give diagrammatic representations called shadow diagrams for knotted surfaces in 4-manifolds. We also provide a low-complexity classification for these structures and describe several examples, including the important case of complex curves inside ℂ ℙ 2 . Using these examples, we prove that there exist exotic 4-manifolds with ( g , 0 ) —trisections for certain values of g. We conclude by sketching a conjectural uniqueness result that would provide a complete diagrammatic calculus for studying knotted surfaces through their shadow diagrams.
Robust functional estimation in the multivariate partial linear model

We consider the problem of adaptive estimation of the functional component in a partial linear model where the argument of the function is defined on a q-dimensional grid. Obtaining an adaptive estimator of this functional component is an important practical problem in econometrics where exact distributions of random errors and the parametric component are mostly unknown. An estimator of the functional component that is adaptive over the wide range of multivariate Besov classes and robust to a wide choice of distributions of the linear component and random errors is constructed. It is also shown that the same estimator is locally adaptive over the same range of Besov classes and robust over large collections of distributions of the linear component and random errors as well. At any fixed point, this estimator attains a local adaptive minimax rate.
A Uniform Generalization of Some Combinatorial Hopf Algebras

We generalize the Hopf algebras of free quasisymmetric functions, quasisymmetric functions, noncommutative symmetric functions, and symmetric functions to certain representations of the category of finite Coxeter systems and its dual category. We investigate their connections with the representation theory of 0-Hecke algebras of finite Coxeter systems. Restricted to type B and D we obtain dual graded modules and comodules over the corresponding Hopf algebras in type A.
On $r$-Uniform Linear Hypergraphs with no Berge-$K_{2,t}$

Let $\mathcal{F}$ be an $r$-uniform hypergraph and $G$ be a multigraph. The hypergraph $\mathcal{F}$ is a Berge-$G$ if there is a bijection $f: E(G) \rightarrow E( \mathcal{F} )$ such that $e \subseteq f(e)$ for each $e \in E(G)$. Given a family of multigraphs $\mathcal{G}$, a hypergraph $\mathcal{H}$ is said to be $\mathcal{G}$-free if for each $G \in \mathcal{G}$, $\mathcal{H}$ does not contain a subhypergraph that is isomorphic to a Berge-$G$. We prove bounds on the maximum number of edges in an $r$-uniform linear hypergraph that is $K_{2,t}$-free. We also determine an asymptotic formula for the maximum number of edges in a linear 3-uniform 3-partite hypergraph that is $\{C_3 , K_{2,3} \}$-free.
Learning to Speed Up Structured Output Prediction

Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained black-box structured classifier without losing accuracy. To do so, we train a speedup classifier that learns to mimic a black-box classifier under the learning-to-search approach. As the structured classifier predicts more examples, the speedup classifier will operate as a learned heuristic to guide search to favorable regions of the output space. We present a mistake bound for the speedup classifier and identify inference situations where it can independently make correct judgments without input features. We evaluate our method on the task of entity and relation extraction and show that the speedup classifier outperforms even greedy search in terms of speed without loss of accuracy.
Fast Binary Embeddings and Quantized Compressed Sensing with Structured Matrices

This paper deals with two related problems, namely distance-preserving binary embeddings and quantization for compressed sensing . First, we propose fast methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$, associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and we associate the cube with a pseudo-metric that approximates Euclidean distance among points in $\mathcal{X}$. Our methods rely on quantizing fast Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and partial circulant ensembles, both of which admit fast transforms. Our quantization methods utilize noise-shaping, and include Sigma-Delta schemes and distributed noise-shaping schemes. The resulting approximation errors decay polynomially and exponentially fast in $m$, depending on the embedding method. This dramatically outperforms the current decay rates associated with binary embeddings and Hamming distances. Additionally, it is the first such binary embedding result that applies to fast Johnson-Lindenstrauss maps while preserving $\ell_2$ norms. Second, we again consider noise-shaping schemes, albeit this time to quantize compressed sensing measurements arising from bounded orthonormal ensembles and partial circulant matrices. We show that these methods yield a reconstruction error that again decays with the number of measurements (and bits), when using convex optimization for reconstruction. Specifically, for Sigma-Delta schemes, the error decays polynomially in the number of measurements, and it decays exponentially for distributed noise-shaping schemes based on beta encoding. These results are near optimal and the first of their kind dealing with bounded orthonormal systems.
Iterative inversion of deformation vector fields with feedback control

Purpose Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. Method We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. Results The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. Conclusion Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods.
Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

Abstract The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption . We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.
Accelerated Consensus for Multi-Agent Networks through Delayed Self Reinforcement

This article aims to improve the performance of networked multi-agent systems, which are common representations of cyber-physical systems. The rate of convergence to consensus of multi-agent networks is critical to ensure cohesive, rapid response to external stimuli. The challenge is that increasing the rate of convergence can require changes in the network connectivity, which might not be always feasible. Note that current consensus-seeking control laws can be considered as a gradient-based search over the graph’s Laplacian potential. The main contribution of this article is to improve the convergence to consensus, by using an accelerated gradient-based search approach. Additionally, this work shows that the accelerated-consensus approach can be implemented in a distributed manner, where each agent applies a delayed self reinforcement, without the need for additional network information or changes to the network connectivity. Simulation result shows that the convergence rate with the accelerated consensus is about double the convergence rate of current consensus laws. Moreover, the loss of synchronization during the transition is reduced by about ten times with the use of the proposed accelerated-consensus approach.
Air pollution exposure and risk of spontaneous abortion in the Nurses' Health Study II.

STUDY QUESTION Is there an association between air pollution exposures and the risk of spontaneous abortion (SAB)? SUMMARY ANSWER Higher exposure to particulate matter (PM) air pollution above and beyond a woman's average exposure may be associated with greater risk of SAB, particularly among women experiencing at least one SAB during follow-up. WHAT IS KNOWN ALREADY There is sufficient biologic plausibility to suggest that air pollution adversely affects early pregnancy outcomes, particularly pregnancy loss; however, the evidence is limited. STUDY DESIGN, SIZE, DURATION Our prospective cohort study included 19 309 women in the Nurses' Health Study II who contributed a total of 35 025 pregnancies between 1990 and 2008. We also conducted a case-crossover analysis among 3585 women (11 212 pregnancies) with at least one SAB and one live birth during follow-up. PARTICIPANTS/MATERIALS, SETTING, METHODS Proximity to major roadways and exposure to PM <10 microns (PM10), 2.5-10 microns (PM2.5-10) and <2.5 microns (PM2.5) were determined for residential addresses between 1989 and 2007. Pregnancy outcomes were self-reported biannually throughout follow-up and comprehensively in 2009. Multivariable log-binomial regression models with generalized estimating equations were used to estimate the risk ratios and 95% CIs of SAB. Conditional logistic regression was used for the case-crossover analysis. MAIN RESULTS AND THE ROLE OF CHANCE During the 19 years of follow-up, 6599 SABs (18.8% of pregnancies) were reported. In the main analysis, living closer to a major roadway and average exposure to PM10, PM10-2.5 or PM2.5 in the 1 or 2 years prior to pregnancy were not associated with an increased risk of SAB. However, small positive associations between PM exposures and SAB were observed when restricting the analysis to women experiencing at least one SAB during follow-up. In the case-crossover analysis, an increase in PM10 (per 3.9 μg/m3), PM2.5-10 (per 2.3 μg/m3) and PM2.5 (per 2.0 μg/m3) in the year prior to pregnancy was associated with 1.12 (95% CI 1.06, 1.19), 1.09 (95% CI 1.03, 1.14) and 1.10 (95% CI 1.04, 1.17) higher odds of SAB, respectively. LIMITATIONS, REASONS FOR CAUTION We did not have information on the month or day of SAB, which precluded our ability to examine specific windows of susceptibility or acute exposures. We also used ambient air pollution exposures as a proxy for personal exposure, potentially leading to exposure misclassification. WIDER IMPLICATIONS OF THE FINDINGS In our case-crossover analysis (but not in the entire cohort) we observed positive associations between exposure to all size fractions of PM exposure and risk of SAB. This may suggest that changes in PM exposure confer greater risk of SAB or that women with a history of SAB are a particularly vulnerable subgroup. STUDY FUNDING/COMPETING INTEREST(S) The authors are supported by the following NIH grants UM1CA176726, R00ES026648 and P30ES000002. The authors have no actual or potential competing financial interests to disclose.
Analysis of Cross-border Data Trade Restrictions using Mixture-based Clustering.

Cross-border data flows and interoperability have created an increasingly digital global trade environment. Concerns for cybersecurity, privacy, and digital sovereignty have prompted the widespread implementation of trade policies regulating cross-border data flows. At present, there is a deficit of information characterizing such policies and analyzing their role in the global economy. We address this deficit through a quantitative analysis of trade policies in services that seeks both to categorize data-related trade policies and to evaluate the extent to which they are different from other restrictive measures. We propose a mixture-based clustering pipeline to group trade restrictiveness data and a method for quantifying the difference between the cross-border data flow regulations and other traditional regulations. Our analysis reveals a significant localization effect among data flow restrictions and that, while highly restrictive data flow policies generally do not overlap with other policies, there is a significant similarity in moderate to liberal policies.
Revisiting the Trade Impact of the African Growth and Opportunity Act : A Synthetic Control Approach

This study examines the impact of the African Growth and Opportunity Act using the synthetic control method, a quasi-experimental approach. The novelty in the approach is that it addresses problems of estimation that are prevalent in nonexperimental methods used to analyze the impact of preferential trade agreements. The findings show that most of the eligible countries registered gains in exports due to the African Growth and Opportunity Act. However, the results are varied, and the gains were largely unsteady. Much of the gains are due to exports of petroleum and other minerals, while there are few countries that were able to expand into manufacturing and other industrial goods. The positive trade impacts were largely associated with improvements in information and communications technology infrastructure, integrity in the institutions of legal and property rights, ease of labor market regulations, and sound macroeconomic environment, including stable exchange rates and low inflation. Undue exposure to a single market, like the United States, or few commodities may have also restricted the gains from trade.
An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets

Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures. Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019 .
On attention models for human activity recognition.

Deep Learning methods have become very attractive in the wider, wearables-based human activity recognition (HAR) research community. The majority of models are based on either convolutional or explicitly temporal models, or combinations of both. In this paper we introduce attention models into HAR research as a data driven approach for exploring relevant temporal context. Attention models learn a set of weights over input data, which we leverage to weight the temporal context being considered to model each sensor reading. We construct attention models for HAR by adding attention layers to a state-of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on benchmark datasets achieving significant increase in performance. Finally, we visualize the learned weights to better understand what constitutes relevant temporal context.
Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks, but also evaluate the potential improvements offered by different planning measures. In this article, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: value-at-risk ( $VaR_\alpha$ ) and conditional value-at-risk ( $CVaR_\alpha$ ) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively, for the events beyond a prespecified risk threshold, $\alpha$ . Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.
The past, present, and future of customer management

For decades, researchers in customer management have demonstrated the business importance of firm-customer relationships, developed models for understanding customer behavior and response to marketing, established the link between customer behavior and firm performance, and proposed policies to optimize customer management activities. This paper overviews customer management research, from its historical origins to its recent developments. We identify promising directions for future research, with an emphasis on novel data sources, online marketplaces, and new technologies; additionally, we highlight the privacy and fairness considerations that come with these developments.
Declination as a Metric to Detect Partisan Gerrymandering

We explore the Declination δ, a new metric intended to detect partisan gerrymandering. We show that when vote share is fixed, δ = 0 allows for a wide array of possible seat shares, even when turnou...
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation

Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and hyperparameter settings. Optimizing these settings by grid or random search is computationally expensive because it requires many training runs. In this paper, we incorporate architecture search into a single training run through auto-sizing, which uses regularization to delete neurons in a network over the course of training. On very low-resource language pairs, we show that auto-sizing can improve BLEU scores by up to 3.9 points while removing one-third of the parameters from the model.
Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.