cssi/SciSciGPT-SciSciNet huggingface dataset:
Robust Speaker Clustering using Mixtures of von Mises-Fisher Distributions for Naturalistic Audio Streams

Speaker Diarization (i.e. determining who spoke and when?) for multi-speaker naturalistic interactions such as Peer-Led Team Learning (PLTL) sessions is a challenging task. In this study, we propose robust speaker clustering based on mixture of multivariate von Mises-Fisher distributions. Our diarization pipeline has two stages: (i) ground-truth segmentation; (ii) proposed speaker clustering. The ground-truth speech activity information is used for extracting i-Vectors from each speechsegment. We post-process the i-Vectors with principal component analysis for dimension reduction followed by lengthnormalization. Normalized i-Vectors are high-dimensional unit vectors possessing discriminative directional characteristics. We model the normalized i-Vectors with a mixture model consisting of multivariate von Mises-Fisher distributions. K-means clustering with cosine distance is chosen as baseline approach. The evaluation data is derived from: (i) CRSS-PLTL corpus; and (ii) three-meetings subset of AMI corpus. The CRSSPLTL data contain audio recordings of PLTL sessions which is student-led STEM education paradigm. Proposed approach is consistently better than baseline leading to upto 44.48% and 53.68% relative improvements for PLTL and AMI corpus, respectively. Index Terms: Speaker clustering, von Mises-Fisher distribution, Peer-led team learning, i-Vector, Naturalistic Audio.
On weaker notions of nonlinear embeddings between Banach spaces

Abstract In this paper, we study nonlinear embeddings between Banach spaces. More specifically, the goal of this paper is to study weaker versions of coarse and uniform embeddability, and to provide suggestive evidences that those weaker embeddings may be stronger than one would think. We do such by proving that many known results regarding coarse and uniform embeddability remain valid for those weaker notions of embeddability.
A note on the localization number of random graphs: diameter two case

Abstract We study the localization game on dense random graphs. In this game, a cop x tries to locate a robber y by asking for the graph distance of y from every vertex in a sequence of sets W 1 , W 2 , … , W l . We prove high probability upper and lower bounds for the minimum size of each W i that will guarantee that x will be able to locate y .
Simion’s Type B Associahedron is a Pulling Triangulation of the Legendre Polytope

We show that the Simion type B associahedron is combinatorially equivalent to a pulling triangulation of the type A root polytope known as the Legendre polytope. Furthermore, we show that every pulling triangulation of the boundary of the Legendre polytope yields a flag complex. Our triangulation refines a decomposition of the boundary of the Legendre polytope given by Cho. Finally, we extend Cho’s cyclic group action to the triangulation in such a way that it corresponds to rotating centrally symmetric triangulations of a regular $$(2n+2)$$ -gon.
Convolution with measures on flat curves in low dimensions

We prove Lp→Lq convolution estimates for the affine arclength measure on certain flat curves in Rd when d∈{2,3,4}. For d=2,3, we also establish certain related Lorentz space estimates.
Rational points of universal curves

In this paper we prove a version of Grothendieck's section conjecture for the restriction of the universal complete curve over M_{g,n}, g > 4, to the function field k(M_{g,n}) where k is, for example, a number field. In this version, the fundamental group of the closed fiber is replaced by its ell-adic unipotent completion when n > 1.
Wolff’s inequality for intrinsic nonlinear potentials and quasilinear elliptic equations

Abstract We prove an analogue of Wolff’s inequality for the so-called intrinsic nonlinear potentials associated with the quasilinear elliptic equation − Δ p u = σ u q in R n , in the case 0 q p − 1 . Here Δ p u = div ( | ∇ u | p − 2 ∇ u ) is the p -Laplacian, and σ is a nonnegative measurable function (or measure). As an application, we give a necessary and sufficient condition for the existence of a positive solution u ∈ L r ( R n ) ( 0 r ∞ ) to this problem, which was open even in the case p = 2 . Our version of Wolff’s inequality for intrinsic nonlinear potentials relies on a new characterization of discrete Littlewood–Paley spaces f p , q ( σ ) defined in terms of characteristic functions of dyadic cubes in R n .
Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to statistical inference is the most complete method for model selection and uncertainty quantification of kinetic parameters from single-cell data. This approach is impractical because current numerical algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piece-wise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We validated our method of accelerated Bayesian inference on several synthetic data sets. Our results show that kinetic parameters can be reasonably constrained for modestly sampled data sets, if the model is known a priori. If the model is unknown,the Bayesian evidence can be used to rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics, e.g., Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), often used for model selection.
On the universality of potential well dynamics

Given a smooth potential function $V : \mathbf{R}^m \to \mathbf{R}$, one can consider the ODE $\partial_t^2 u = -(\nabla V)(u)$ describing the trajectory of a particle $t \mapsto u(t)$ in the potential well $V$. We consider the question of whether the dynamics of this family of ODE are \emph{universal} in the sense that they contain (as embedded copies) any first-order ODE $\partial_t u = X(u)$ arising from a smooth vector field $X$ on a manifold $M$. Assuming that $X$ is nonsingular and $M$ is compact, we show (using the Nash embedding theorem) that this is possible precisely when the flow $(M,X)$ supports a geometric structure which we call a \emph{strongly adapted $1$-form}; many smooth flows do have such a $1$-form, but we give an example (due to Bryant) of a flow which does not, and hence cannot be modeled by the dynamics of a potential well. As one consequence of this embeddability criterion, we construct an example of a (coercive) potential well system which is \emph{Turing complete} in the sense that the halting of any Turing machine with a given input is equivalent to a certain bounded trajectory in this system entering a certain open set. In particular, this system contains trajectories for which it is undecidable whether that trajectory enters such a set. Remarkably, the above results also hold if one works instead with the nonlinear wave equation $\partial_t^2 u - \Delta u = -(\nabla V)(u)$ on a torus instead of a particle in a potential well, or if one replaces the target domain $\mathbf{R}^m$ by a more general Riemannian manifold.
Error Correction Coding Meets Cyber-Physical Systems: Message-Passing Analysis of Self-Healing Interdependent Networks

Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, which can lead to large-scale cascading failures. However, the self-healing ability emerges as a valuable opportunity where the overlaying cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, this paper considers a graphical model representation of an interdependent cyber-physical system, in which nodes represent various cyber or physical functionalities, and edges capture the interactions between the nodes. A message-passing algorithm is proposed for this representation to study the dynamics of failure propagation and healing. By conducting a density evolution analysis for this algorithm, network reaction to initial disruptions is investigated. It is proved that as the number of message-passing iterations increases, the network reaches a steady-state condition that would be either a complete healing or a complete collapse. Then, a sufficient condition is derived to select the network parameters to guarantee the complete healing of the system. The result of the density evolution analysis is further employed to jointly optimize the design of cyber and physical networks for maximum resiliency. This analytical framework is then extended to the cases where the propagation of failures in the physical network is faster than the healing responses of the cyber network. Such scenarios are of interest in many real-life applications such as smart grid. Finally, extensive numerical results are presented to verify the analysis and investigate the impact of the network parameters on the resiliency of the network.
Online dynamic mode decomposition for time-varying systems

Dynamic mode decomposition (DMD) is a popular technique for modal decomposition, flow analysis, and reduced-order modeling. In situations where a system is time varying, one would like to update th...
FORMING THE BORROMEAN RINGS OUT OF ARBITRARY POLYGONAL UNKNOTS

We prove that given any three polygonal unknots in ℝ3, then we may form the Borromean rings out of them through rigid motions of ℝ3 applied to the individual components together with possible scaling of the components. We also prove that if at least two of the unknots are planar, then we do not need scaling. This is true even for a set of three polygonal unknots that are arbitrarily close to three circles, which themselves cannot be used to form the Borromean rings.
Central limit theorems for some set partition statistics

We prove the conjectured limiting normality for the number of crossings of a uniformly chosen set partition of n = { 1 , 2 , ? , n } . The arguments use a novel stochastic representation and are also used to prove central limit theorems for the dimension index and the number of levels.
A new space for comparing graphs

Finding1 a new mathematical representation for graphs, which allows direct comparison between different graph structures, is an open-ended research direction. Having such a representation is the first prerequisite for a variety of machine learning algorithms like classification, clustering, etc., over graph datasets. In this paper, we propose a symmetric positive semidefinite matrix with the (i, j)-th entry equal to the covariance between normalized vectors Aie and Aje (e being vector of all ones) as a representation for a graph with adjacency matrix A. We show that the proposed matrix representation encodes the spectrum of the underlying adjacency matrix and it also contains information about the counts of small sub-structures present in the graph such as triangles and small paths. In addition, we show that this matrix is a "graph invariant". All these properties make the proposed matrix a suitable object for representing graphs.The representation, being a covariance matrix in a fixed dimensional metric space, gives a mathematical embedding for graphs. This naturally leads to a measure of similarity on graph objects. We define similarity between two given graphs as a Bhattacharya similarity measure between their corresponding covariance matrix representations. As shown in our experimental study on the task of social network classification, such a similarity measure outperforms other widely used state-of-the-art methodologies. Our proposed method is also computationally efficient. The computation of both the matrix representation and the similarity value can be performed in operations linear in the number of edges. This makes our method scalable in practice.We believe our theoretical and empirical results provide evidence for studying truncated power iterations, of the adjacency matrix, to characterize social networks.
Spectral learning of large structured HMMs for comparative Epigenomics

We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by connecting their hidden states by a fixed tree of known structure. The main challenge with learning parameters of such models is that iterative methods such as EM are very slow, while naive spectral methods result in time and space complexity exponential in the number of cell types. We exploit properties of the tree structure of the hidden states to provide spectral algorithms that are more computationally efficient for current biological datasets. We provide sample complexity bounds for our algorithm and evaluate it experimentally on biological data from nine human cell types. Finally, we show that beyond our specific model, some of our algorithmic ideas can be applied to other graphical models.
A Formal Approach to the Problem of Logical Non-Omniscience.

We present the logical induction criterion for computable algorithms that assign probabilities to every logical statement in a given formal language, and refine those probabilities over time. The criterion is motivated by a series of stock trading analogies. Roughly speaking, each logical sentence phi is associated with a stock that is worth $1 per share if phi is true and nothing otherwise, and we interpret the belief-state of a logically uncertain reasoner as a set of market prices, where pt_N(phi)=50% means that on day N, shares of phi may be bought or sold from the reasoner for 50%. A market is then called a logical inductor if (very roughly) there is no polynomial-time computable trading strategy with finite risk tolerance that earns unbounded profits in that market over time. We then describe how this single criterion implies a number of desirable properties of bounded reasoners; for example, logical inductors outpace their underlying deductive process, perform universal empirical induction given enough time to think, and place strong trust in their own reasoning process.
INFINITE HILBERT CLASS FIELD TOWERS FROM GALOIS REPRESENTATIONS

We investigate class field towers of number fields obtained as fixed fields of modular representations of the absolute Galois group of the rational numbers. First, for each k ∈ {12, 16, 18, 20, 22, 26}, we give explicit rational primes l such that the fixed field of the mod-l representation attached to the unique normalized cusp eigenform of weight k on SL2(ℤ) has an infinite class field tower. Further, under a conjecture of Hardy and Littlewood, we prove the existence of infinitely many cyclotomic fields of prime conductor, providing infinitely many such primes l for each k in the list. Finally, given a non-CM curve E/ℚ, we show that there exists an integer ME such that the fixed field of the representation attached to the n-division points of E has an infinite class field tower for a set of integers n of density one among integers coprime to ME.
On the error of random fourier features

Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning. The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets. Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features. The quality of this approximation, however, is not well understood. We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods. We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds.
Permutation Totally Symmetric Self-Complementary Plane Partitions

Alternating sign matrices and totally symmetric self-complementary plane partitions are equinumerous sets of objects for which no explicit bijection is known. In this paper, we identify a subset of totally symmetric self-complementary plane partitions corresponding to permutations by giving a statistic-preserving bijection to permutation matrices, which are a subset of alternating sign matrices. We use this bijection to define a new partial order on permutations, and prove this new poset contains both the Tamari lattice and the Catalan distributive lattice as subposets. We also study a new partial order on totally symmetric self-complementary plane partitions arising from this perspective and show that this is a distributive lattice related to Bruhat order when restricted to permutations.
Nonuniqueness of weak solutions to the SQG equation

We prove that weak solutions of the inviscid SQG equations are not unique, thereby answering Open Problem 11 in the survey arXiv:1111.2700 by De Lellis and Szekelyhidi Jr. Moreover, we also show that weak solutions of the dissipative SQG equation are not unique, even if the fractional dissipation is stronger than the square root of the Laplacian.
When is a subgroup of a ring an ideal

Let R be a commutative ring. When is a subgroup of.R;C/ an ideal of R? We investigate this problem for the rings Z d and Q dD1Zni . In the cases of Z Z and Zn Zm, our results give, for any given subgroup of these rings, a computable criterion for the problem under consideration. We also compute the probability that a randomly chosen subgroup from Zn Zm is an ideal.
Moment-based Uniform Deviation Bounds for k-means and Friends

Suppose k centers are fit to m points by heuristically minimizing the k-means cost; what is the corresponding fit over the source distribution? This question is resolved here for distributions with p ≥ 4 bounded moments; in particular, the difference between the sample cost and distribution cost decays with m and p as mmin{-1/4,-1/2+2/p}. The essential technical contribution is a mechanism to uniformly control deviations in the face of unbounded parameter sets, cost functions, and source distributions. To further demonstrate this mechanism, a soft clustering variant of k-means cost is also considered, namely the log likelihood of a Gaussian mixture, subject to the constraint that all covariance matrices have bounded spectrum. Lastly, a rate with refined constants is provided for k-means instances possessing some cluster structure.
The dynamics of supertranslations and superrotations in 2 + 1 dimensions

Author(s): Carlip, S | Abstract: Supertranslations, and at least in 2 + 1 dimensions superrotations, are asymptotic symmetries of the metric in asymptotically flat spacetimes. They are not, however, symmetries of the boundary term of the Einstein-Hilbert action, which therefore induces an action for the Goldstone-like fields that parametrize these symmetries. I show that in 2 + 1 dimensions, this action is closely related to a chiral Liouville action, as well as the 'Schwarzian' action that appears in two-dimensional near-AdS physics.
Carrier Lifetime Enhancement in a Tellurium Nanowire/PEDOT:PSS Nanocomposite by Sulfur Passivation

We report static and time-resolved terahertz (THz) conductivity measurements of a highperformance thermoelectric material containing tellurium nanowires in a PEDOT:PSS matrix. Composites were made with and without sulfur passivation of the nanowires surfaces. The material with sulfur linkers (TeNW/PD-S) is less conductive but has a longer carrier lifetime than the formulation without (TeNW/PD). We find real conductivities at f = 1THz of σTeNW/PD = 160 S/cm and σTeNW/PD-S = 5.1 S/cm. These values are much larger than the corresponding DC conductivities, suggesting DC conductivity is limited by structural defects. The free-carrier lifetime in the nanowires is controlled by recombination and trapping at the nanowire surfaces. We find surface recombination velocities in bare tellurium nanowires (22m/s) and TeNW/PD-S (40m/s) that are comparable to evaporated tellurium thin films. The surface recombination velocity in TeNW/PD (509m/s) is much larger, indicating a higher interface trap density.
Essential Dimension and the Flats Spanned by a Point Set

Let P be a finite set of points in ℝd or ℂd.We answer a question of Purdy on the conditions under which the number of hyperplanes spanned by P is at least the number of (d−2)-flats spanned by P. In answering this question, we define a new measure of the degeneracy of a point set with respect to affine subspaces, termed the essential dimension. We use the essential dimension to give an asymptotic expression for the number of k-flats spanned by P, for 1≤k≤d−1.
Shared Autonomy via Hindsight Optimization.

In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user’s intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user’s goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user’s goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly.
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power

Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing constraints. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 20%, 40%, 60% and 80% prediction intervals which are evaluated using the pinball loss function and reliability measures. Three benchmark models are used for comparison where results demonstrate the proposed approach leads to significantly better performance while preventing the problem of overlapping quantile estimates.
Dirichlet process mixtures of generalized mallows models

We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large real-world ranking datasets.
Storage Enforcement with Kolmogorov Complexity and List Decoding

We consider the following problem that arises in outsourced storage: a user stores her data x on a remote server but wants to audit the server at some later point to make sure it actually did store x. The goal is to design a (randomized) verification protocol that has the property that if the server passes the verification with some reasonably high probability then the user can rest assured that the server is storing x. While doing this, we need to minimize the user’s storage and the total amount of communication between the server and the user. This is the data possession problem and is closely related to the problem of obtaining a ’proof of retrievability.’ Existing schemes with provable guarantees mostly use cryptographic primitives and can only guarantee that the server is storing a constant fraction of the amount of information that it is supposed to store. In this work we present an optimal solution (in terms of the user’s storage and communication) while at the same time ensuring that a server that passes the verification protocol with any reasonable probability will store, to within a small additive factor, C(x) bits of information, where C(x) is the plain Kolmogorov complexity of x. (Since we cannot prevent the server from compressing x, C(x) is a natural upper bound.) The proof of security of our protocol combines Kolmogorov complexity with list decoding and unlike previous work that relies upon cryptographic assumptions, we allow the server to have unlimited computational power. To the best of our knowledge, this is the first work that combines Kolmogorov complexity and list decoding. Our framework is general enough to capture extensions where the user splits up x and stores the fragment across multiple servers and our verification protocol can handle non-responsive servers and colluding servers. As a by-product, we also get a proof of retrievability. Finally, our results also have an application in ‘storage enforcement’ schemes, which in turn have an application in trying to update a remote server that is potentially infected with a virus.
CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.
Implementing a Portable Clinical NLP System with a Common Data Model -- a Lisp Perspective

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.
Investigation of Steady-State and Time-dependent Luminescence Properties of Colloidal InGaP Quantum Dots.

Quantum dots play a promising role in the development of novel optical and biosensing devices. In this study, we investigated steady state and time-dependent luminescence properties of InGaP/ZnS core/shell colloidal quantum dots in a solution phase at room temperature. The steady state experiments exhibited an emission maximum at 650 nm with full width at half maximum of ~ 85 nm, and strong first-excitonic absorption peak at 600 nm. The time-resolved luminescence measurements depicted a bi-exponential decay profile with lifetimes of τ1 ~ 47 ns and τ2 ~ 142 ns at the emission maximum. Additionally, luminescence quenching and lifetime reduction due to resonance energy transfer between the quantum dot and an absorber are demonstrated. Our results support the plausibility of using these InGaP quantum dots as an effective alternative to highly toxic conventional Cd or Pb based colloidal quantum dots for biological applications.
Exact and stable recovery of rotations for robust synchronization

The problem has found applications in computer vision, computer graphics, and sensor network localization, among others. Its least squares solution can be approximated by either spectral relaxation or semidefinite programming followed by a rounding procedure, analogous to the approximation algorithms of MAX-CUT. The contribution of this paper is three-fold: First, we introduce a robust penalty function involving the sum of unsquared deviations and derive a relaxation that leads to a convex optimization problem; Second, we apply the alternating direction method to minimize the penalty function; Finally, under a specific model of the measurement noise and for both complete and random measurement graphs, we prove that the rotations are exactly and stably recovered, exhibiting a phase transition behavior in terms of the proportion of noisy measurements. Numerical simulations confirm the phase transition behavior for our method as well as its improved accuracy compared to existing methods.
Digital IP protection using threshold voltage control

This paper proposes a method to completely hide the functionality of a digital standard cell. This is accomplished by a differential threshold logic gate (TLG). A TLG with n inputs implements a subset of Boolean functions of n variables that are linear threshold functions. The output of such a gate is one if and only if an integer weighted linear arithmetic sum of the inputs equals or exceeds a given integer threshold. We present a novel architecture of a TLG that not only allows a single TLG to implement a large number of complex logic functions, which would require multiple levels of logic when implemented using conventional logic primitives, but also allows the selection of that subset of functions by assignment of the transistor threshold voltages to the input transistors. To obfuscate the functionality of the TLG, weights of some inputs are set to zero by setting their device threshold to be a high Vt. The threshold voltage of the remaining transistors is set to low Vt to increase their transconductance. The number of low Vt transistors whose gates are driven by a given input xi determines the weight of that input. The function of a TLG is not determined by the cell itself but rather the signals that are connected to its inputs. This makes it possible to hide the support set of the function by essentially removing some variable from the support set of the function. This is done by selective assignment of high and low Vt to the input transistors. We describe how a standard cell library of TLGs can be mixed with conventional standard cells to realize complex logic circuits, whose function can never be discovered by reverse engineering. A 32-bit Wallace tree multiplier and a 28-bit 4-tap filter were synthesized on an ST 65nm process, placed and routed, then simulated including extracted parastics with and without obfuscation. By obfuscating the cells the delay was shown to increase by approximately 5% at the cell level. Both obfuscated designs had much lower area (25%) lower area and much lower dynamic power (30%) than their nonobfuscated CMOS counterparts, operating at the same frequency.
Learning DNF Expressions from Fourier Spectrum

Since its introduction by Valiant in 1984, PAC learning of DNF expressions remains one of the central problems in learning theory. We consider this problem in the setting where the underlying distribution is uniform, or more generally, a product distribution. Kalai, Samorodnitsky, and Teng (2009b) showed that in this setting a DNF expression can be efficiently approximated from its “heavy” low-degree Fourier coefficients alone. This is in contrast to previous approaches where boosting was used and thus Fourier coefficients of the target function modified by various distributions were needed. This property is crucial for learning of DNF expressions over smoothed product distributions, a learning model introduced by Kalai et al. (2009b) and inspired by the seminal smoothed analysis model of Spielman and Teng (2004). We introduce a new approach to learning (or approximating) a polynomial threshold functions which is based on creating a function with range [1; 1] that approximately agrees with the unknown function on low-degree Fourier coefficients. We then describe conditions under which this is sufficient for learning polynomial threshold functions. Our approach yields a new, simple algorithm for approximating any polynomial-size DNF expression from its “heavy” low-degree Fourier coefficients alone. This algorithm greatly simplifies the proof of learnability of DNF expressions over smoothed product distributions and is simpler than all previous algorithm for PAC learning of DNF expression using membership queries. We also describe an application of our algorithm to learning monotone DNF expressions over product distributions. Building on the work of Servedio (2004), we give an algorithm that runs in time poly((s log (s=)) log (s=) ;n), wheres is the size of the DNF expression and is the accuracy. This improves on poly((s log (ns=)) log (s=)log (1 =) ;n)
Gamed-based iSTART practice: From MiBoard to self-explanation showdown

MiBoard (Multiplayer Interactive Board Game) is an online, turn-based board game that was developed to assess the integration of game characteristics (point rewards, game-like interaction, and peer feedback) and how that might affect student engagement and learning efficacy. This online board game was designed to fit within the Extended Practice module of iSTART (Interactive Strategy Training for Active Reading and Thinking). Unfortunately, preliminary research shows that MiBoard actually reduces engagement and does not benefit the quality of student self-explanations when compared to the original Extended Practice module. Consequently the MiBoard framework has been revamped to create Self-Explanation Showdown, a faster-paced, less analytically oriented game that adds competition to the creation of self-explanations. Students are evaluated on the quality of their self-explanations using the same assessment algorithms from iSTART Extended Practice module (this includes both word-based and LSA-based assessments). The technical issues involved in development of MiBoard and Self-Explanation Showdown are described. The lessons learned from the MiBoard experience are also discussed in this paper.
Analysis of magic lengths in growth of supported metallic nanowires

Metallic nanowires can exhibit fascinating physical properties. These unique properties often originate primarily from the quantum confinement of free electrons in a potential well, while electron–electron interactions do not play a decisive role. A recent experimental study shows that self-assembled Ir nanowires grown on Ge(001) surface have a strong length preference: the nanowire lengths are an integer multiple of 4.8 nm. In this paper, a free-electron-gas model for geometries corresponding to the nanowires is used to analyze the selection of these preferred or magic lengths. The model shows that the inclusion of even numbers of free electrons in an Ir nanowire produces these magic lengths once an electron spillage effect is taken into account. The model also shows that the stability of the nanowire diminishes with its increasing length, and consequently suggests why no long nanowires are observed in experiments. It is also shown that applying generic results for quantum size effects in a nanofilm geometry is not adequate to accurately describe the length selection in the rather different nanowire geometry, where the transverse dimensions are smaller than the electron Fermi wavelength. Finally, monatomic Au chain growth on Ge(001) surface is also analyzed. In contrast to Ir nanowires, the model shows that the stability of an Au chain depends strongly on the extent of electron spillage.
On the topology of weakly and strongly separated set complexes

Abstract We examine the topology of the clique complexes of the graphs of weakly and strongly separated subsets of the set [ n ] = { 1 , 2 , … , n } , which, after deleting all cone points, we denote by Δ ˆ ws ( n ) and Δ ˆ ss ( n ) , respectively. In particular, we find that Δ ˆ ws ( n ) is contractible for n ⩾ 4 , while Δ ˆ ss ( n ) is homotopy equivalent to a sphere of dimension n − 3 . We also show that our homotopy equivalences are equivariant with respect to the group generated by two particular symmetries of Δ ˆ ws ( n ) and Δ ˆ ss ( n ) : One induced by the set complementation action on subsets of [ n ] and another induced by the action on subsets of [ n ] which replaces each k ∈ [ n ] by n + 1 − k .
A sequential approximation framework for coded distributed optimization

Building on the previous work of Lee et al. [2] and Ferdinand et al. [3] on coded computation, we propose a sequential approximation framework for solving optimization problems in a distributed manner. In a distributed computation system, latency caused by individual processors (“stragglers”) usually causes a significant delay in the overall process. The proposed method is powered by a sequential computation scheme, which is designed specifically for systems with stragglers. This scheme has the desirable property that the user is guaranteed to receive useful (approximate) computation results whenever a processor finishes its subtask, even in the presence of uncertain latency. In this paper, we give a coding theorem for sequentially computing matrix-vector multiplications, and the optimality of this coding scheme is also established. As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.
Economic sizing of distributed energy resources for reliable community microgrids

Community microgrids are developed within existing distribution systems. When there is an extreme event happening, distribution systems can be seamlessly partitioned into several community microgrids for uninterrupted supply to the endusers. In order to guarantee the system reliability, distributed energy resources (DERs) should be sized for ensuring generation adequacy to cover unexpected events. This paper presents a comprehensive methodology for DERs selection in community microgrids, and an economic approach to meet the system reliability requirements. Algorithms of discrete time Fourier transform (DTFT) and particle swarm optimization (PSO) are employed to find the optimal solution. Uncertainties of load demand and renewable generation are taken into consideration. As part of the case study, a sensitivity analysis is carried out to show the renewable generation impact on DERs' capacity planning.
Localization for Chern-Simons on circle bundles via loop groups

Abstract We consider Chern–Simons theory on a 3-manifold M that is the total space of a circle bundle over a 2d base Σ . We show that this theory is equivalent to a new 2d TQFT on the base, which we call Caloron BF theory, that can be obtained by an appropriate type of push-forward. This is a gauge theory on a bundle with structure group given by the full affine level k central extension of the loop group L G . The space of fields of this 2d theory is naturally symplectic, and this provides a new formulation of a result of Beasley–Witten about the equivariant localization of the Chern–Simons path integral. The main tool that we employ is the Caloron correspondence, originally due to Murray–Garland, that relates the space of gauge fields on M with a certain enlarged space of connections on an equivariant version of the loop space of the G -bundle. We show that the symplectic structure that Beasley–Witten found is related to a looped version of the Atiyah–Bott construction in 2-dimensional Yang–Mills theory. We also show that Wilson loops that wrap a single circle fiber are also described very naturally in this framework.
Bounding cubic-triple product Selmer groups of elliptic curves

Let E be a modular elliptic curve over a totally real cubic field. We have a cubic-triple product motive over Q constructed from E through multiplicative induction; it is of rank 8. We show that, under certain assumptions on E, the non-vanishing of the central critical value of the L-function attached to the motive implies that the dimension of the associated Bloch-Kato Selmer group is 0.
Biologically Unavoidable Sequences

A biologically unavoidable sequence is an infinite gender sequence which occurs in every gendered, infinite genealogical network satisfying certain tame conditions. We show that every eventually periodic sequence is biologically unavoidable (this
Theoretically-grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer

Policy advice is a transfer learning method where a student agent is able to learn faster via advice from a teacher. However, both this and other reinforcement learning transfer methods have little theoretical analysis. This paper formally defines a setting where multiple teacher agents can provide advice to a student and introduces an algorithm to leverage both autonomous exploration and teacher's advice. Our regret bounds justify the intuition that good teachers help while bad teachers hurt. Using our formalization, we are also able to quantify, for the first time, when negative transfer can occur within such a reinforcement learning setting.
A semidefinite program for unbalanced multisection in the stochastic block model

We propose a semidefinite programming (SDP) algorithm for community detection in the stochastic block model, a popular model for networks with latent community structure. We prove that our algorithm achieves exact recovery of the latent communities, up to the information-theoretic limits determined by Abbe and Sandon. Our result extends prior SDP approaches by allowing for many communities of different sizes. By virtue of a semidefinite approach, our algorithms succeed against a semirandom variant of the stochastic block model, guaranteeing a form of robustness and generalization. We further explore how semirandom models can lend insight into both the strengths and limitations of SDPs in this setting.
MotifMark: Finding regulatory motifs in DNA sequences

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein binding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.
Galois descent of additive invariants

Making use of the recent theory of noncommutative motives, we prove that every additive invariant satisfies Galois descent. Examples include mixed complexes, Hochschild homology, cyclic homology, periodic cyclic homology, negative cyclic homology, connective algebraic K-theory, mod-l algebraic K-theory, nonconnective algebraic K-theory, homotopy K-theory, topological Hochschild homology, and topological cyclic homology.
Quincke rotor dynamics in confinement: rolling and hovering

The Quincke effect is an electrohydrodynamic instability which gives rise to a torque on a dielectric particle in a uniform DC electric field. Previous studies reported that a sphere initially resting on the electrode rolls with steady velocity. We experimentally find that in strong fields the rolling becomes unsteady, with time-periodic velocity. Furthermore, we find another regime, where the rotating sphere levitates in the space between the electrodes. Our experimental results show that the onset of Quincke rotation strongly depends on particle confinement and the threshold for rolling is higher compared to rotation in the hovering state.
Non-vanishing of Dirichlet series without Euler products

We give a new proof that the Riemann zeta function is nonzero in the half-plane {s ∈ C : σ > 1}. A novel feature of this proof is that it makes no use of the Euler product for ζ(s).