cssi/SciSciGPT-SciSciNet huggingface dataset:
Detection of low-density Plasmodium falciparum infections using amplicon deep sequencing

Deep sequencing of targeted genomic regions is becoming a common tool for understanding the dynamics and complexity of Plasmodium infections, but its lower limit of detection is currently unknown. Here, a new amplicon analysis tool, the Parallel Amplicon Sequencing Error Correction (PASEC) pipeline, is used to evaluate the performance of amplicon sequencing on low-density Plasmodium DNA samples. Illumina-based sequencing of two Plasmodium falciparum genomic regions (CSP and SERA2) was performed on two types of samples: in vitro DNA mixtures mimicking low-density infections (1–200 genomes/μl) and extracted blood spots from a combination of symptomatic and asymptomatic individuals (44–653,080 parasites/μl). Three additional analysis tools—DADA2, HaplotypR, and SeekDeep—were applied to both datasets and the precision and sensitivity of each tool were evaluated. Amplicon sequencing can contend with low-density samples, showing reasonable detection accuracy down to a concentration of 5 Plasmodium genomes/μl. Due to increased stochasticity and background noise, however, all four tools showed reduced sensitivity and precision on samples with very low parasitaemia (< 5 copies/μl) or low read count (< 100 reads per amplicon). PASEC could distinguish major from minor haplotypes with an accuracy of 90% in samples with at least 30 Plasmodium genomes/μl, but only 61% at low Plasmodium concentrations (< 5 genomes/μl) and 46% at very low read counts (< 25 reads per amplicon). The four tools were additionally used on a panel of extracted parasite-positive blood spots from natural malaria infections. While all four identified concordant patterns of complexity of infection (COI) across four sub-Saharan African countries, COI values obtained for individual samples differed in some cases. Amplicon deep sequencing can be used to determine the complexity and diversity of low-density Plasmodium infections. Despite differences in their approach, four state-of-the-art tools resolved known haplotype mixtures with similar sensitivity and precision. Researchers can therefore choose from multiple robust approaches for analysing amplicon data, however, error filtration approaches should not be uniformly applied across samples of varying parasitaemia. Samples with very low parasitaemia and very low read count have higher false positive rates and call for read count thresholds that are higher than current default recommendations.
Vigour/tolerance trade‐off in cultivated sunflower (Helianthus annuus) response to salinity stress is linked to leaf elemental composition

Developing more stress-tolerant crops will require greater knowledge of the physiological basis of stress tolerance. Here we explore how the variation among twenty cultivated sunflower (Helianthus annuus) genotypes for biomass decline in response to increasing salinity relates to leaf traits and leaf trait adjustments. Genotypes were grown in the greenhouse under five salinity treatments (0, 50, 100, 150, or 200 mM NaCl) for 21 days and assessed for growth, leaf physiological traits, and leaf elemental composition. Results showed that there was a trade-off in performance such that vigorous genotypes, higher biomass at zero mM NaCl, had both a larger absolute decrease and proportional decrease in biomass due to increased salinity. Contrary to expectation, genotypes with a low increase in leaf Na+ and Na+:K+ were no better at maintaining biomass with increasing salinity. Rather, genotypes with a greater reduction in leaf S and K+ content were better at maintaining biomass in the face of increasing salinity. While we found a trade-off between vigour and tolerance, some genotypes were more tolerant than expected. Further analysis of the traits underlying this trade-off will allow us to identify traits/mechanisms that could be bred into high vigour genotypes in order to increase their tolerance.
EUCLIDEAN FORMULATION OF DISCRETE UNIFORMIZATION OF THE DISK

Thurston's circle packing approximation of the Riemann Mapping (proven to give the Riemann Mapping in the limit by Rodin-Sullivan) is largely based on the theorem that any topological disk with a circle packing metric can be deformed into a circle packing metric in the disk with boundary cir- cles internally tangent to the circle. The main proofs of the uniformization use hyperbolic volumes (Andreev) or hyperbolic circle packings (by Beardon and Stephenson). We reformulate these problems into a Euclidean context, which allows more general discrete conformal structures and boundary condi- tions. The main idea is to replace the disk with a double covered disk with one side forced to be a circle and the other forced to have interior curvature zero. The entire problem is reduced to finding a zero curvature structure. We also show that these curvatures arise naturally as curvature measures on gen- eralized manifolds (manifolds with multiplicity) that extend the usual discrete Lipschitz-Killing curvatures on surfaces.
An Affordable Bio-Sensing and Activity Tagging Platform for HCI Research

We present a novel multi-modal bio-sensing platform capable of integrating multiple data streams for use in real-time applications. The system is composed of a central compute module and a companion headset. The compute node collects, time-stamps and transmits the data while also providing an interface for a wide range of sensors including electroencephalogram, photoplethysmogram, electrocardiogram, and eye gaze among others. The companion headset contains the gaze tracking cameras. By integrating many of the measurements systems into an accessible package, we are able to explore previously unanswerable questions ranging from open-environment interactions to emotional-response studies. Though some of the integrated sensors are designed from the ground-up to fit into a compact form factor, we validate the accuracy of the sensors and find that they perform similarly to, and in some cases better than, alternatives.
Contributions to persistence theory

Persistence theory discussed in this paper is an appli- cation of algebraic topology (Morse Theory (29)) to Data Analysis, precisely to qualitative understanding of point cloud data, or PCD for short. PCD can be geometrized as a filtration of simplicial com- plexes (Vietoris-Rips complex (25) (36)) and the homology changes of these complexes provide qualitative information about the data. Bar codes describe the changes in homology with coefficients in a fixed field. When the coefficient field is Z2, the calculation of bar codes is done by ELZ algorithm (named after H. Edelsbrunner, D. Letscher, and A. Zomorodian (20)). When the coefficient field is R, we propose an algorithm based on the Hodge decomposi- tion (17). With Dan Burghelea and Tamal K. Dey we developed a persistence theory which involves level sets discussed in Section 4. We introduce and discuss new computable invariants, the "rel- evant level persistence numbers" and the "positive and negative bar codes", and explain how they are related to the bar codes for level persistence. We provide enhancements and modifications of ELZ algorithm to calculate such invariants and illustrate them by examples.
On Legendrian Surgeries

We use the Ozsv\'ath-Szab\'o contact invariants to distinguish between tight contact structures obtained by Legendrian surgeries on stabilized Legendrian links in tight contact $3$-manifolds. We also discuss the implication of our result on the tight contact structures on the Brieskorn homology spheres $-\Sigma(2,3,6n-1)$.
Global formulations of Lagrangian and Hamiltonian mechanics on two-spheres

This paper provides global formulations of Lagrangian and Hamiltonian variational dynamics evolving on the product of an arbitrary number of two-spheres. Four types of Euler-Lagrange equations and Hamilton's equations are developed in a coordinate-free fashion on two-spheres, without relying on local parameterizations that may lead to singularities and cumbersome equations of motion. The proposed intrinsic formulations of Lagrangian and Hamiltonian dynamics are novel in that they incorporate the geometry of two-spheres, resulting in equations of motion that are expressed compactly, and they are useful in analysis and computation of the global dynamics.
Active Distribution Learning from Indirect Samples

This paper studies the problem of learning the probability distribution $P_{X}$ of a discrete random variable X using indirect and sequential samples. At each time step, we choose one of the possible K functions, $g_{1}, \cdots, g_{K}$ and observe the corresponding sample $g_{i}(X)$. The goal is to estimate the probability distribution of X by using a minimum number of such sequential samples. This problem has several real-world applications including inference under non-precise information and privacy-preserving statistical estimation. We establish necessary and sufficient conditions on the functions $g_{1}, \cdots, g_{K}$ under which asymptotically consistent estimation is possible. We also derive lower bounds on the estimation error as a function of total samples and show that it is order-wise achievable. Leveraging these results, we propose an iterative algorithm that i) chooses the function to observe at each step based on past observations; and ii) combines the obtained samples to estimate $p_{X}$. The performance of this algorithm is investigated numerically under various scenarios, and shown to outperform baseline approaches.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites.

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs.
The strong slope conjecture and torus knots

We show that the strong slope conjecture implies that the degree of the colored Jones polynomial detects all torus knots. As an application we obtain that an adequate knot that has the same colored Jones polynomial degrees as a torus knot must be a $(2,q)$-torus knot.
Information Maximizing Visual Question Generation

Though image-to-sequence generation models have become overwhelmingly popular in human-computer communications, they suffer from strongly favoring safe generic questions (``What is in this picture?''). Generating uninformative but relevant questions is not sufficient or useful. We argue that a good question is one that has a tightly focused purpose --- one that is aimed at expecting a specific type of response. We build a model that maximizes mutual information between the image, the expected answer and the generated question. To overcome the non-differentiability of discrete natural language tokens, we introduce a variational continuous latent space onto which the expected answers project. We regularize this latent space with a second latent space that ensures clustering of similar answers. Even when we don't know the expected answer, this second latent space can generate goal-driven questions specifically aimed at extracting objects (``what is the person throwing''), attributes, (``What kind of shirt is the person wearing?''), color (``what color is the frisbee?''), material (``What material is the frisbee?''), etc. We quantitatively show that our model is able to retain information about an expected answer category, resulting in more diverse, goal-driven questions. We launch our model on a set of real world images and extract previously unseen visual concepts.
Tarski-type problems for free associative algebras

Abstract In this paper we study fundamental model-theoretic questions for free associative algebras, namely, first-order classification, decidability of the first-order theory, and definability of the set of free bases. We show that two free associative algebras of finite rank over fields, at least one of which is infinite, are elementarily equivalent if and only if either their ranks are finite and equal or both are infinite and the fields are equivalent in the weak second order logic. In particular, two free associative algebras of finite rank over the same field are elementarily equivalent if and only if they are isomorphic. We prove that if an arbitrary ring B with at least one Noetherian proper centralizer is first-order equivalent to a free associative algebra of finite rank over an infinite field then B is also a free associative algebra of finite rank over a field. This solves the elementary classification problem for free associative algebras in a wide class of rings (in fact, we note that a finitely generated algebra for which this result would not solve the classification problem would be a true monster). We also present a formula of the pure ring language which defines the set of free bases in a free associative algebra A K ( X ) with finite basis X over an infinite field K . To prove this result we establish rather deep connections between first order theory of A K ( X ) and the weak second order theory of the field K . In fact, we prove that A K ( X ) and the weak second order theory of K (the superstructure H F ( K ) ) are bi-interpretable in each other. Finally, we show that the arithmetical hierarchy over A K ( X ) is proper, in particular there is no any reasonable “quantifier elimination” in free associative algebras.
A Note On Immersion Intertwines Of Infinite Graphs

We present a construction of two infinite graphs $G_1$ and $G_2$, and of an infinite set $\mathscr{F}$ of graphs such that $\mathscr{F}$ is an antichain with respect to the immersion relation and, for each graph $G$ in $\mathscr{F}$, both $G_1$ and $G_2$ are subgraphs of $G$, but no graph properly immersed in $G$ admits an immersion of $G_1$ and of $G_2$. This shows that the class of infinite graphs ordered by the immersion relation does not have the finite intertwine property.
PERMUTATION-TWISTED MODULES FOR EVEN ORDER CYCLES ACTING ON TENSOR PRODUCT VERTEX OPERATOR SUPERALGEBRAS

We construct and classify (1 2 ⋯ k)-twisted V⊗k-modules for k even and V a vertex operator superalgebra. In particular, we show that the category of weak (1 2 ⋯ k)-twisted V⊗k-modules for k even is isomorphic to the category of weak parity-twisted V-modules. This result shows that in the case of a cyclic permutation of even order, the construction and classification of permutation-twisted modules for tensor product vertex operator superalgebras are fundamentally different than in the case of a cyclic permutation of odd order, as previously constructed and classified by the first author. In particular, in the even order case it is the parity-twisted V-modules that play the significant role in place of the untwisted V-modules that play the significant role in the odd order case.
Hierarchical Implicit Models and Likelihood-Free Variational Inference

Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for models which encompass our understanding of the physical word. Despite this fundamental nature, the use of implicit models remains limited due to challenge in positing complex latent structure in them, and the ability to inference in such models with large data sets. In this paper, we first introduce the hierarchical implicit models (HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian modeling thereby defining models via simulators of data with rich hidden structure. Next, we develop likelihood-free variational inference (LFVI), a scalable variational inference algorithm for HIMs. Key to LFVI is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for symbol generation.
Scalable Electric Vehicle Charging Protocols

Although electric vehicles are considered a viable solution to reduce greenhouse gas emissions, their uncoordinated charging could have adverse effects on power system operation. Nevertheless, the task of optimal electric vehicle charging scales unfavorably with the fleet size and the number of control periods, especially when distribution grid limitations are enforced. To this end, vehicle charging is first tackled using the recently revived Frank–Wolfe method. The novel decentralized charging protocol has minimal computational requirements from vehicle controllers, enjoys provable acceleration over existing alternatives, enhances the security of the pricing mechanism against data attacks, and protects user privacy. To comply with voltage limits, a network-constrained EV charging problem is subsequently formulated. Leveraging a linearized model for unbalanced distribution grids, the goal is to minimize the power supply cost while respecting critical voltage regulation and substation capacity limitations. Optimizing variables across grid nodes is accomplished by exchanging information only between neighboring buses via the alternating direction method of multipliers. Numerical tests corroborate the optimality and efficiency of the novel schemes.
An asymptotic expansion for the expected number of real zeros of real random polynomials spanned by OPUC

Abstract Let { φ i } i = 0 ∞ be a sequence of orthonormal polynomials on the unit circle with respect to a positive Borel measure μ that is symmetric with respect to conjugation. We study asymptotic behavior of the expected number of real zeros, say E n ( μ ) , of random polynomials P n ( z ) : = ∑ i = 0 n η i φ i ( z ) , where η 0 , … , η n are i.i.d. standard Gaussian random variables. When μ is the acrlength measure such polynomials are called Kac polynomials and it was shown by Wilkins that E n ( | d ξ | ) admits an asymptotic expansion of the form E n ( | d ξ | ) ∼ 2 π log ⁡ ( n + 1 ) + ∑ p = 0 ∞ A p ( n + 1 ) − p (Kac himself obtained the leading term of this expansion). In this work we generalize the result of Wilkins to the case where μ is absolutely continuous with respect to arclength measure and its Radon–Nikodym derivative extends to a holomorphic non-vanishing function in some neighborhood of the unit circle. In this case E n ( μ ) admits an analogous expansion with the coefficients A p depending on the measure μ for p ≥ 1 (the leading order term and A 0 remain the same).
Properties of Carathéodory measure hyperbolic universal covers of compact Kähler manifolds

This article explores some properties of universal covers of compact Kahler manifolds, under the assumption of Caratheodory measure hyperbolicity. In particular, by comparing invariant volume forms, an inequality is established between the volume of canonical bundle of a compact Kahler manifolds and the Caratheodory measure of its universal cover (similar result as in [Kikuta 10]). Using similar method, an inequality is established between the restricted volume of canonical bundle of a compact Kahler manifolds and the restricted Caratheodory measure of its covering, solving a conjecture in [Kikuta 13].
Graphs, links, and duality on surfaces

We introduce a polynomial invariant of graphs on surfaces, PG, generalizing the classical Tutte polynomial. Topological duality on surfaces gives rise to a natural duality result for PG, analogous to the duality for the Tutte polynomial of planar graphs. This property is important from the perspective of statistical mechanics, where the Tutte polynomial is known as the partition function of the Potts model. For ribbon graphs, PG specializes to the well-known Bollobas–Riordan polynomial, and in fact the two polynomials carry equivalent information in this context. Duality is also established for a multivariate version of the polynomial PG. We then consider a 2-variable version of the Jones polynomial for links in thickened surfaces, taking into account homological information on the surface. An analogue of Thistlethwaite's theorem is established for these generalized Jones and Tutte polynomials for virtual links.
Primes in short intervals on curves over finite fields

We prove an analogue of the Prime Number Theorem for short intervals on a smooth projective geometrically irreducible curve of arbitrary genus over a finite field. A short interval “of size E” in this setting is any additive translate of the space of global sections of a sufficiently positive divisor E by a suitable rational function f. Our main theorem gives an asymptotic count of irreducible elements in short intervals on a curve in the “large q” limit, uniformly in f and E. This result provides a function field analogue of an unresolved short interval conjecture over number fields, and extends a theorem of Bary-Soroker, Rosenzweig, and the first author, which can be understood as an instance of our result for the special case of a divisor E supported at a single rational point on the projective line.
Free Monotone Transport for Infinite Variables

We extend the free monotone transport theorem of Guionnet and Shlyakhtenko to the case of infinite variables. As a first application, we provide a criterion for when mixed $q$-Gaussian algebras are isomorphic to $L(\mathbb{F}_\infty)$; namely, when the structure array $Q$ of a mixed $q$-Gaussian algebra has uniformly small entries that decay sufficiently rapidly. Here a mixed $q$-Gaussian algebra with structure array $Q=(q_{ij})_{i,j\in\mathbb{N}}$ is the von Neumann algebra generated by $X_n^Q=l_n+l_n^*, n\in\mathbb{N}$ and $(l_n)$ are the Fock space representations of the commutation relation $l_i^*l_j-q_{ij}l_jl_i^*=\delta_{i=j}, i,j\in\mathbb{N}$, $-1<q_{ij}=q_{ji}<1$.
Highest Weights for Categorical Representations

We present a criterion for establishing Morita equivalence of monoidal categories, and apply it to the categorical representation theory of reductive groups $G$. We show that the "de Rham group algebra" $\mathcal D(G)$ (the monoidal category of $\mathcal D$-modules on $G$) is Morita equivalent to the universal Hecke category $\mathcal D(N \backslash G/N)$ and to its monodromic variant $\widetilde{\mathcal D}(B \backslash G / B)$. In other words, de Rham $G$-categories, i.e., module categories for $\mathcal D(G)$, satisfy a "highest weight theorem" - they all appear in the decomposition of the universal principal series representation $\mathcal D(G/N)$ or in twisted $\mathcal D$-modules on the flag variety $\widetilde{\mathcal D}(G/B)$
On a class of bias-amplifying variables that endanger effect estimates

This note deals with a class of variables that, if conditioned on, tends to amplify confounding bias in the analysis of causal effects. This class, independently discovered by Bhattacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treatment selection than on the outcome. We offer a simple derivation and an intuitive explanation of this phenomenon and then extend the analysis to non linear models. We show that: 1. the bias-amplifying potential of instrumental variables extends over to nonlinear models, though not as sweepingly as in linear models; 2. in non-linear models, conditioning on instrumental variables may introduce new bias where none existed before; 3. in both linear and non-linear models, instrumental variables have no effect on selection-induced bias.
Swendsen-Wang dynamics for general graphs in the tree uniqueness region

The Swendsen-Wang dynamics is a popular algorithm for sampling from the Gibbs distribution for the ferromagnetic Ising model on a graph $G=(V,E)$. The dynamics is a "global" Markov chain which is conjectured to converge to equilibrium in $O(|V|^{1/4})$ steps for any graph $G$ at any (inverse) temperature $\beta$. It was recently proved by Guo and Jerrum (2017) that the Swendsen-Wang dynamics has polynomial mixing time on any graph at all temperatures, yet there are few results providing $o(|V|)$ upper bounds on its convergence time. We prove fast convergence of the Swendsen-Wang dynamics on general graphs in the tree uniqueness region of the ferromagnetic Ising model. In particular, when $\beta < \beta_c(d)$ where $\beta_c(d)$ denotes the uniqueness/non-uniqueness threshold on infinite $d$-regular trees, we prove that the relaxation time (i.e., the inverse spectral gap) of the Swendsen-Wang dynamics is $\Theta(1)$ on any graph of maximum degree $d \geq 3$. Our proof utilizes a version of the Swendsen-Wang dynamics which only updates isolated vertices. We establish that this variant of the Swendsen-Wang dynamics has mixing time $O(\log{|V|})$ and relaxation time $\Theta(1)$ on any graph of maximum degree $d$ for all $\beta < \beta_c(d)$. We believe that this Markov chain may be of independent interest, as it is a monotone Swendsen-Wang type chain. As part of our proofs, we provide modest extensions of the technology of Mossel and Sly (2013) for analyzing mixing times and of the censoring result of Peres and Winkler (2013). Both of these results are for the Glauber dynamics, and we extend them here to general monotone Markov chains. This class of dynamics includes for example the heat-bath block dynamics, for which we obtain new tight mixing time bounds.
Asymptotic normality of in- and out-degree counts in a preferential attachment model

ABSTRACTPreferential attachment in a directed scale-free graph is an often used paradigm for modeling the evolution of social networks. Social network data is usually given in a format allowing recovery of the number of nodes with in-degree i and out-degree j. Assuming a model with preferential attachment, formal statistical procedures for estimation can be based on such data summaries. Anticipating the statistical need for such node-based methods, we prove asymptotic normality of the node counts. Our approach is based on a martingale construction and a martingale central limit theorem.
Isoperimetry with upper mean curvature bounds and sharp stability estimates

It was proved by Almgren that among boundaries whose mean curvature is bounded from above, perimeter is uniquely minimized by balls. We obtain sharp stability estimates for Almgren’s isoperimetric principle and, as an application, we deduce a sharp description of boundaries with almost constant mean curvature under a total perimeter bound which prevents bubbling.
A Fredholm operator approach to Morita equivalence

Given C∗-algebras A and B and an imprimitivity A-B-bimodule X, we construct an explicit isomorphism X∗:Ki(A) → Ki(B) where Ki denote the complexK-theory functors for i = 0, 1. Our techniques do not require separability nor existence of countable approximate identities. We thus extend, to general C∗-algebras, the result of Brown, Green and Rieffel according to which strongly Morita equivalent C∗-algebras have isomorphic K-groups. The method employed includes a study of Fredholm operators on Hilbert modules.
Parametric t-Distributed Stochastic Exemplar-centered Embedding

Parametric embedding methods such as parametric t-distributed Stochastic Neighbor Embedding (pt-SNE) enables out-of-sample data visualization without further computationally expensive optimization or approximation. However, pt-SNE favors small mini-batches to train a deep neural network but large mini-batches to approximate its cost function involving all pairwise data point comparisons, and thus has difficulty in finding a balance. To resolve the conflicts, we present parametric t-distributed stochastic exemplar-centered embedding. Our strategy learns embedding parameters by comparing training data only with precomputed exemplars to indirectly preserve local neighborhoods, resulting in a cost function with significantly reduced computational and memory complexity. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep feedforward neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed method significantly outperforms pt-SNE in terms of robustness, visual effects, and quantitative evaluations.
Private Causal Inference

Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few. However, these promising applications for causal inference are often ones that involve sensitive or personal data of users that need to be kept private (e.g., medical records, personal finances, etc). Therefore, there is a need for the development of causal inference methods that preserve data privacy. We study the problem of inferring causality using the current, popular causal inference framework, the additive noise model (ANM) while simultaneously ensuring privacy of the users. Our framework provides differential privacy guarantees for a variety of ANM variants. We run extensive experiments, and demonstrate that our techniques are practical and easy to implement.
Certified Defenses for Data Poisoning Attacks

Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. While recent work has proposed a number of attacks and defenses, little is understood about the worst-case loss of a defense in the face of a determined attacker. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Our approximation relies on two assumptions: (1) that the dataset is large enough for statistical concentration between train and test error to hold, and (2) that outliers within the clean (non-poisoned) data do not have a strong effect on the model. Our bound comes paired with a candidate attack that often nearly matches the upper bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Empirically, we find that even under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to attack, while in contrast the IMDB sentiment dataset can be driven from 12% to 23% test error by adding only 3% poisoned data.
Multiple Network Alignment via MultiMAGNA

Network alignment (NA) aims to find a node mapping that identifies topologically or functionally similar network regions between molecular networks of different species. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA method called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ outperforms or is on par with existing MNA methods, while often completing faster than existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more fair MNA method comparison compared to the existing alignment quality measures. The multiMAGNA++ code is available on the method's web page at http://nd.edu/~cone/multiMAGNA++/ .
Keyless authentication in the presence of a simultaneously transmitting adversary

If Alice must communicate with Bob over a channel shared with the adversarial Eve, then Bob must be able to validate the authenticity of the message. In particular we consider the model where Alice and Eve share a discrete memoryless multiple access channel with Bob, thus allowing simultaneous transmissions from Alice and Eve. By traditional random coding arguments, we demonstrate an inner bound on the rate at which Alice may transmit, while still granting Bob the ability to authenticate. Furthermore this is accomplished in spite of Alice and Bob lacking a pre-shared key, as well as allowing Eve prior knowledge of both the codebook Alice and Bob share and the messages Alice transmits.
Phase retrieval from very few measurements

Abstract In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper focuses on completely determining signals with as few intensity measurements as possible, and on efficient phase retrieval algorithms from such measurements. For the case of complex M-dimensional signals, we construct a measurement ensemble of size 4 M − 4 which yields injective intensity measurements; this is conjectured to be the smallest such ensemble. For the case of real signals, we devise a theory of “almost” injective intensity measurements, and we characterize such ensembles. Later, we show that phase retrieval from M + 1 almost injective intensity measurements is NP -hard, indicating that computationally efficient phase retrieval must come at the price of measurement redundancy.
A note on Lagrangian loci of quotients

We study Hamiltonian actions of compact groups in the presence of compatible involutions. We show that the Lagrangian fixed point set on the symplectically reduced space is isomorphic to the disjoint union of the involutively reduced spaces corresponding to involutions on the group strongly inner to the given one. Our techniques imply that the solution to the eigenvalues of a sum problem for a given real form can be reduced to the quasi-split real form in the same inner class. We also consider invariant quotients with respect to the corresponding real form of the complexified group.
Efficient compressive phase retrieval with constrained sensing vectors

We propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially. In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex relaxations for the type of structure in the target. Given a set of underdetermined measurements, there is a standard framework for recovering a sparse matrix, and a standard framework for recovering a low-rank matrix. However, a general, efficient method for recovering a jointly sparse and low-rank matrix has remained elusive. Deviating from the models with generic measurements, in this paper we show that if the sensing vectors are chosen at random from an incoherent subspace, then the low-rank and sparse structures of the target signal can be effectively decoupled. We show that a recovery algorithm that consists of a low-rank recovery stage followed by a sparse recovery stage will produce an accurate estimate of the target when the number of measurements is O(k log d/k), where k and d denote the sparsity level and the dimension of the input signal. We also evaluate the algorithm through numerical simulation.
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.
Taylor–Duffy Method for Singular Tetrahedron-Product Integrals: Efficient Evaluation of Galerkin Integrals for VIE Solvers

This paper presents an accurate and efficient technique for the numerical evaluation of singular six-dimensional (6-D) integrals over tetrahedron-product domains, with applications to the calculation of Galerkin matrix elements for discretized volume-integral-equation (VIE) solvers using Schaubert–Wilton–Glisson (SWG) and other tetrahedral basis functions. The proposed method extends the generalized Taylor–Duffy strategy—used to handle the singular triangle -product integrals arising in discretized surface-integral-equation formulations—to the tetrahedron-product case; it effects an exact transformation of a singular 6-D integral into a nonsingular lower dimensional integral, which may be evaluated by simple numerical cubature. The method is very general and may be applied—with the aid of automatic code generation facilitated by computer-algebra systems— to a wide variety of singular integrals arising in various volume-integral-equation (VIE) formulations with various types of tetrahedral basis functions, several examples of which are presented in this paper. To demonstrate the accuracy and efficiency of the method, it has been applied in the calculation of matrix elements for the volume electric-field integral equation discretized with SWG basis functions, where the method yields 12-digit or higher accuracy with a low computational cost—an improvement of many orders of magnitude compared to existing techniques.
Enabling Robots to Infer How End-Users Teach and Learn Through Human-Robot Interaction

During human-robot interaction, we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we can leverage interactions, where the human and robot observe each other's behavior. However, it is not always clear how the human and robot should interpret these actions: a given interaction might mean several different things. Within today's state of the art, the robot assigns a single interaction strategy to the human, and learns from or teaches the human according to this fixed strategy. Instead, we here recognize that different users interact in different ways, and so one size does not fit all. Therefore, we argue that the robot should maintain a distribution over the possible human interaction strategies, and then infer how each individual end-user interacts during the task. We formally define learning and teaching when the robot is uncertain about the human's interaction strategy, and derive solutions to both problems using Bayesian inference. In examples and a benchmark simulation, we show that our personalized approach outperforms standard methods that maintain a fixed interaction strategy.
On Jones’ subgroup of R. Thompson’s group T

Jones introduced unitary representations for the Thompson groups F and T from a given subfactor planar algebra. Some interesting subgroups arise as the stabilizer of certain vector, in particular t...
Low-Sensitivity Functions from Unambiguous Certificates.

We provide new query complexity separations against sensitivity for total Boolean functions: a power 3 separation between deterministic (and even randomized or quantum) query complexity and sensitivity, and a power 2.22 separation between certificate complexity and sensitivity. We get these separations by using a new connection between sensitivity and a seemingly unrelated measure called one-sided unambiguous certificate complexity. We also show that one-sided unambiguous certificate complexity is lower-bounded by fractional block sensitivity, which means we cannot use these techniques to get a super-quadratic separation between block sensitivity and sensitivity. Along the way, we give a power 1.22 separation between certificate complexity and one-sided unambiguous certificate complexity, improving the power 1.128 separation due to Goos [FOCS 2015]. As a consequence, we obtain an improved lower-bound on the co-nondeterministic communication complexity of the Clique vs. Independent Set problem.
Approximate separability for weak interaction in dynamic systems

One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction called separability, and showed that it leads to exact propagation of marginals for prediction. This paper addresses two questions left open by the earlier paper: can we define a notion of approximate separability that occurs naturally in practice, and do separability and approximate separability lead to accurate monitoring? The answer to both questions is affirmative. The paper also analyzes the structure of approximately separable decompositions, and provides some explanation as to why these models perform well.
Sample complexity of episodic fixed-horizon reinforcement learning

Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample complexity bounds. However, in many real-world applications, an interactive learning agent operates for a fixed or bounded period of time, for example tutoring students for exams or handling customer service requests. Such scenarios can often be better treated as episodic fixed-horizon MDPs, for which only looser bounds on the sample complexity exist. A natural notion of sample complexity in this setting is the number of episodes required to guarantee a certain performance with high probability (PAC guarantee). In this paper, we derive an upper PAC bound O(|S|2|A|H2/∊2 ln 1/δ) and a lower PAC bound Ω(|S||A|H2/∊2 ln 1/δ+c) that match up to log-terms and an additional linear dependency on the number of states |S|. The lower bound is the first of its kind for this setting. Our upper bound leverages Bernstein's inequality to improve on previous bounds for episodic finite-horizon MDPs which have a time-horizon dependency of at least H3.
Relationship between blocking temperature and strength of interparticle interaction in magnetic nanoparticle systems

Abstract In magnetic nanoparticle systems, the variation of the blocking temperature T B with the measuring frequency f m is often used to determine the strength of the interparticle interactions (IPI) through a parameter Φ or the Vogel-Fulcher temperature T o . Presence of IPI is inferred if T o > 0 and Φ = ∆ T B [ T B ∆ log 10 f m ] 0.13 where Δ signifies changes in T B and f m . Here it is shown that these two parameters are related by the Eq. Φ = Φ o [ 1 − ( T o T B ( 1 ) ) ] where Φ o ≈ 0.11–0.15 is a constant of the system depending on the magnitudes of measuring frequency and the attempt frequency f o of the Neel relaxation. Using data on a variety of nanoparticle systems, experimental verification of this relationship is also presented.
The Place of the Trace: Negligence and Responsibility

One popular theory of moral responsibility locates responsible agency in exercises of control. These control-based theories often appeal to tracing to explain responsibility in cases where some agent is intuitively responsible for bringing about some outcome despite lacking direct control over that outcome’s obtaining. Some question whether control-based theories are committed to utilizing tracing to explain responsibility in certain cases. I argue that reflecting on certain kinds of negligence shows that tracing plays an ineliminable role in any adequate control-based theory of responsibility.
Improved Proximity, Contact, and Force Sensing via Optimization of Elastomer-Air Interface Geometry

We describe a single fingertip-mounted sensing system for robot manipulation that provides proximity (pre-touch), contact detection (touch), and force sensing (post-touch). The sensor system consists of optical time-of-flight range measurement modules covered in a clear elastomer. Because the elastomer is clear, the sensor can detect and range nearby objects, as well as measure deformations caused by objects that are in contact with the sensor and thereby estimate the applied force. We examine how this sensor design can be improved with respect to invariance to object reflectivity, signal-to-noise ratio, and continuous operation when switching between the distance and force measurement regimes. By harnessing time-of-flight technology and optimizing the elastomer-air boundary to control the emitted light’s path, we develop a sensor that is able to seamlessly transition between measuring distances of up to 50 mm and contact forces of up to 10 newtons. We demonstrate that our sensor improves manipulation accuracy in a block unstacking task. Thorough instructions for manufacturing the sensor from inexpensive, commercially available components are provided, as well as all relevant hardware design files and software sources.