cssi/SciSciGPT-SciSciNet huggingface dataset:
Rationalizing Neural Predictions

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications -- rationales -- that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.
Upper Bounds for Stern's Diatomic Sequence and Related Sequences

Let $(s_2(n))_{n=0}^\infty$ denote Stern's diatomic sequence. For $n\geq 2$ , we may view $s_2(n)$ as the number of partitions of $n-1$ into powers of $2$ with each part occurring at most twice. More generally, for integers $b,n\geq 2$ , let $s_b(n)$ denote the number of partitions of $n-1$ into powers of $b$ with each part occurring at most $b$ times. Using this combinatorial interpretation of the sequences $s_b(n)$ , we use the transfer-matrix method to develop a means of calculating $s_b(n)$ for certain values of $n$ . This then allows us to derive upper bounds for $s_b(n)$ for certain values of $n$ . In the special case $b=2$ , our bounds improve upon the current upper bounds for the Stern sequence. In addition, we are able to prove that $\displaystyle{\limsup_{n\rightarrow\infty}\frac{s_b(n)}{n^{\log_b\phi}}=\frac{(b^2-1)^{\log_b\phi}}{\sqrt 5}}$ .
Embedding feature selection for large-scale hierarchical classification

Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this work, we investigate various filter-based feature selection methods for dimensionality reduction to solve the large-scale HC problem. Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy. Source Code: https://cs.gmu.edu/∼mlbio/featureselection.
A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System

We introduce a game-theoretic framework to explore revenue sharing in an Edge-Cloud computing system, in which computing service providers at the edge of the Internet (edge providers) and computing service providers at the cloud (cloud providers) collectively provide computing resources to clients (e.g., end users or applications) at the edge. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. To achieve high system-level efficiency, the manager of the system adopts a task distribution mechanism to maximize the total revenue received from clients and also adopts a revenue sharing mechanism to split the received revenue among computing servers (and hence service providers). Under those system-level mechanisms, service providers attempt to game with the system in order to maximize their own utilities, by strategically allocating their resources (e.g., computing servers). Our framework models the competition among the providers in an Edge-Cloud system as a non-cooperative game. We have shown the existence of Nash equilibrium in the game both theoretically and practically through simulations and experiments on an emulation system that we have developed. We find that revenue sharing mechanisms have a significant impact on the system-level efficiency at Nash equilibria, and surprisingly the revenue sharing mechanism based directly on actual contributions can result in significantly worse system performance than Shapley value sharing mechanism and Ortmann proportional sharing mechanism. Our framework provides an effective economics approach to the understanding and designing of efficient Edge-Cloud computing systems.
The Sine-Gordon Equation in the Semiclassical Limit: Dynamics of Fluxon Condensates

Introduction Formulation of the inverse problem for fluxon condensates Elementary transformations of $\mathbf{J}(w)$ Construction of $g(w)$ Use of $g(w)$ Appendix A. Proofs of propositions concerning initial data Appendix B. Details of the outer parametrix in cases $\mathsf{L}$ and $\mathsf{R}$ Bibliography
An all-encompassing global convergence result for processive multisite phosphorylation systems.

Phosphorylation, the enzyme-mediated addition of a phosphate group to a molecule, is a ubiquitous chemical mechanism in biology. Multisite phosphorylation, the addition of phosphate groups to multiple sites of a single molecule, may be distributive or processive. Distributive systems, which require an enzyme and substrate to bind several times in order to add multiple phosphate groups, can be bistable. Processive systems, in contrast, require only one binding to add all phosphate groups, and were recently shown to be globally stable. However, this global convergence result was proven only for a specific mechanism of processive phosphorylation/dephosphorylation (namely, all catalytic reactions are reversible). Accordingly, we generalize this result to allow for processive phosphorylation networks in which each reaction may be irreversible, and also to account for possible product inhibition. We accomplish this by first defining an all-encompassing processive network that encapsulates all of these schemes, and then appealing to recent results of Marcondes de Freitas et al. that assert global convergence by way of monotone systems theory and network/graph reductions (corresponding to removing intermediate complexes). Our results form a case study into the question of when global convergence is preserved when reactions and/or intermediate complexes are added to or removed from a network.
On the Zeros of a Class of Modular Functions

We generalize a number of works on the zeros of certain level 1 modular forms to a class of weakly holomorphic modular functions whose q-expansions satisfy the following: $$\begin{aligned} f_k(A; \tau )\, {:}{=}\,q^{-k}(1+a(1)q+a(2)q^2+\cdots ) + O(q), \end{aligned}$$ where a(n) are numbers satisfying a certain analytic condition. We show that the zeros of such $$f_k(\tau )$$ in the fundamental domain of $$\text {SL}_2(\mathbb {Z})$$ lie on $$|\tau |=1$$ and are transcendental. We recover as a special case earlier work of Witten on extremal “partition” functions $$Z_k(\tau )$$ . These functions were originally conceived as possible generalizations of constructions in three-dimensional quantum gravity.
When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies

We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user's cookie, and further to the user's real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user's entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.
Genus bounds for min-max minimal surfaces

We prove optimal genus bounds for minimal surfaces arising from the min-max construction of Simon–Smith. This confirms a conjecture made by Pitts–Rubinstein in 1986.
Comprehensive Personalized Ranking Using One-Bit Comparison Data

The task of a personalization system is to recommend items or a set of items according to the users’ taste, and thus predicting their future needs. In this paper, we address such personalized recommendation problems for which one-bit comparison data of user preferences for different items as well as the different user inclinations toward an item are available. We devise a comprehensive personalized ranking (CPR) system by employing a Bayesian treatment. We also provide a connection to the learning method with respect to the CPR optimization criterion to learn the underlying low-rank structure of the rating matrix based on the well-established matrix factorization method. Numerical results are provided to verify the performance of our algorithm.
Hierarchical clustering of asymmetric networks

This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure, output hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter. Our construction of hierarchical clustering methods is built around the concept of admissible methods, which are those that abide by the axioms of value—nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them—and transformation—when dissimilarities are reduced, the network may become more clustered but not less. Two particular methods, termed reciprocal and nonreciprocal clustering, are shown to provide upper and lower bounds in the space of admissible methods. Furthermore, alternative clustering methodologies and axioms are considered. In particular, modifying the axiom of value such that clustering in two-node networks occurs at the minimum of the two dissimilarities entails the existence of a unique admissible clustering method. Finally, the developed clustering methods are implemented to analyze the internal migration in the United States.
Canonical Thurston obstructions for sub-hyperbolic semi-rational branched coverings

We prove that the canonical Thurston obstruction for a sub-hyperbolic semi-rational branched covering exists if the branched covering is not CLH-equivalent to a rational map.
Parameters optimization of deep learning models using Particle swarm optimization

Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high-quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users' tasks in setting these important parameters. Nonetheless, these two techniques can be very time-consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experiments indicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77 % – 85%. In fact, the PSO yields even better accuracy results.
On the influence of momentum acceleration on online learning

This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic gradient method with a re-scaled (larger) step-size value. The equivalence result is established for all time instants and not only in steady-state. The analysis is carried out for general risk functions, and is not limited to quadratic risks. One notable conclusion is that the well-known benefits of momentum constructions for deterministic optimization problems do not necessarily carry over to the stochastic setting when gradient noise is present and continuous adaptation is necessary. The analysis suggests a method to enhance performance in the stochastic setting by tuning the momentum parameter over time.
An Efficient List Decoder Architecture for Polar Codes

Long polar codes can achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels under a low-complexity successive cancelation (SC) decoding algorithm. However, for polar codes with short and moderate code lengths, the decoding performance of the SC algorithm is inferior. The cyclic-redundancy-check (CRC)-aided SC-list (SCL)-decoding algorithm has better error performance than the SC algorithm for short or moderate polar codes. In this paper, we propose an efficient list decoder architecture for the CRC-aided SCL algorithm, based on both algorithmic reformulations and architectural techniques. In particular, an area efficient message memory architecture is proposed to reduce the area of the proposed decoder architecture. An efficient path pruning unit suitable for large list size is also proposed. For a polar code of length 1024 and rate 1/2, when list size $L=2$ and 4, the proposed list decoder architecture is implemented under a Taiwan Semiconductor Manufacturing Company (TSMC) 90-nm CMOS technology. Compared with the list decoders in the literature, our decoder achieves 1.24–1.83 times the area efficiency.
Hadamard Matrices Modulo p and Small Modular Hadamard Matrices

We use modular symmetric designs to study the existence of Hadamard matrices modulo certain primes. We solve the 7-modular and 11-modular versions of the Hadamard conjecture for all but a finite number of cases. In doing so, we state a conjectural sufficient condition for the existence of a p-modular Hadamard matrix for all but finitely many cases. When 2 is a primitive root of a prime p, we conditionally solve this conjecture and therefore the p-modular version of the Hadamard conjecture for all but finitely many cases when , and prove a weaker result for . Finally, we look at constraints on the existence of m-modular Hadamard matrices when the size of the matrix is small compared to m.
Gromov hyperbolicity and the Kobayashi metric on convex domains of finite type

In this paper we prove necessary and sufficient conditions for the Kobayashi metric on a convex domain to be Gromov hyperbolic. In particular we show that for convex domains with \(C^\infty \) boundary being of finite type in the sense of D’Angelo is equivalent to the Gromov hyperbolicity of the Kobayashi metric. We also show that bounded domains which are locally convexifiable and have finite type in the sense of D’Angelo have Gromov hyperbolic Kobayashi metric. The proofs use ideas from the theory of the Hilbert metric.
On the $U_p$ operator in characteristic $p$

For a perfect field \kappa of characteristic p>0, a positive ingeger N not divisible by p, and an arbitrary subgroup \Gamma of GL_2(Z/NZ), we prove (with mild additional hypotheses when p\le 3) that the U-operator on the space M_k(\Gamma/\kappa) of (Katz) modular forms for \Gamma over \kappa induces a surjection U:M_{k}(\Gamma/\kappa)\rightarrow M_{k'}(\Gamma/\kappa) for all k\ge p+2, where k'=(k-k_0)/p + k_0 with 2\le k_0\le p+1 the unique integer congruent to k modulo p. When \kappa=F_p, p\ge 5, N\neq 2,3, and \Gamma is the subgroup of upper-triangular or upper-triangular unipotent matrices, this recovers a recent result of Dewar.
Context-aware Data Aggregation with Localized Information Privacy

In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users’ privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion Uy explicitly modeling the adversary's knowledge. However, it is stricter than 2ϵ-LDP and ϵ-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. We then present an optimization framework for privacy-preserving data aggregation, with the goal of minimizing the expected squared error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under several models in closed-form. We then validate our conclusions by numerical analysis using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.
Rank and Nielsen equivalence in hyperbolic extensions

In this note, we generalize a theorem of Juan Souto on rank and Nielsen equivalence in the fundamental group of a hyperbolic fibered 3-manifold to a large class of hyperbolic group extensions. This...
Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.
Paraconsistency and word puzzles

Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuru et al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, called Annotated Predicate Calculus (APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, called consistency preferred stable models and argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchs et al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebser et al. 2011) and its Asprin add-on (Brewka et al. 2015).
Some remarks on bounded earthquakes

We show that an earthquake of a geometrically infinite hyperbolic surface induces an asymptotically conformal change in the hyperbolic metric if and only if the measured lamination associated with the earthquake is asymptotically trivial on the surface. Then we show that the contraction along earthquake paths is continuous in the Teichmuller space of any hyperbolic surface. Finally, we show that if a measured lamination vanishes while approaching infinity at a rate higher than the distance to the boundary, then it must be trivial.
Possibilistic fuzzy local information C-Means for sonar image segmentation

Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor. When viewing this imagery, a human observer can often easily identify various sea-floor textures such as sand ripple, hard-packed sand, sea grass and rock. In this paper, we present the Possibilistic Fuzzy Local Information C-Means (PFLICM) approach to segment SAS imagery into sea-floor regions that exhibit these various natural textures. The proposed PFLICM method incorporates fuzzy and possibilistic clustering methods and leverages (local) spatial information to perform soft segmentation. Results are shown on several SAS scenes and compared to alternative segmentation approaches.
Dynamic data-driven Bayesian GMsFEM

Abstract In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabilistically in a Generalized multiscale finite element method framework. Due to scale disparity in many multiscale applications, computational models cannot resolve all scales. Dominant modes in the Generalized Multiscale Finite Element Method are used as “permanent” basis functions, which we use to compute an inexpensive multiscale solution and the associated uncertainties. Through our Bayesian framework, we can model approximate solutions by selecting the unresolved scales probabilistically. We consider parabolic equations in heterogeneous media. The temporal domain is partitioned into subintervals. Using residual information and given dynamic data, we design appropriate prior distribution for modeling missing subgrid information. The likelihood is designed to minimize the residual in the underlying PDE problem and the mismatch of observational data. Using the resultant posterior distribution, the sampling process identifies important degrees of freedom beyond permanent basis functions. The method adds important degrees of freedom in resolving subgrid information and ensuring the accuracy of the observations.
A Symbolic Decision Procedure for Symbolic Alternating Finite Automata

Abstract We introduce Symbolic Alternating Finite Automata (s-AFA) as a succinct and decidable model for describing sets of finite sequences over arbitrary alphabets. Boolean operations over s-AFAs have linear complexity, which contrasts the quadratic cost of intersection and union for non-alternating symbolic automata. Due to this succinctness, emptiness and equivalence checking are PSpace-hard. We introduce an algorithm for checking the equivalence of two s-AFAs based on bisimulation up to congruence. This algorithm exploits the power of SAT solvers to efficiently search the state space of the s-AFAs. We evaluate our decision procedure on two verification and security applications: 1) checking satisfiability of linear temporal logic formulae over finite traces, and 2) checking equivalence of Boolean combinations of regular expressions. Our experiments show that our technique can be beneficial in both applications.
Gramian-Based Optimization for the Analysis and Control of Traffic Networks

This paper proposes a simplified version of classical models for urban traffic networks and studies the problem of optimizing the network overall efficiency by controlling the signalized intersections. Differently from traditional approaches to control traffic signaling, the tractability of our framework allows us to effectively model large-scale interconnections and enables the design of critical parameters while considering network-wide measures of efficiency. Motivated by the increasing availability of real-time high-resolution traffic data, we cast an optimization problem that formalizes the goal of optimizing vehicle evacuation by controlling the durations of green lights at the intersections under the current congestion conditions. Our framework allows us to relate efficiency objectives with the optimization of a metric of controllability of the associated dynamical network. We then provide a technique to efficiently solve the optimization by parallelizing the computation among a group of distributed agents. Last, we assess the benefits of the proposed modeling and optimization framework through macroscopic and microscopic simulations on daily commute scenarios for the urban interconnection of Manhattan, NY, USA.
The Importance of Communities for Learning to Influence

We consider the canonical problem of influence maximization in social networks. Since the seminal work of Kempe, Kleinberg, and Tardos there have been two, largely disjoint efforts on this problem. The first studies the problem associated with learning the generative model that produces cascades, and the second focuses on the algorithmic challenge of identifying a set of influencers, assuming the generative model is known. Recent results on learning and optimization imply that in general, if the generative model is not known but rather learned from training data, no algorithm for influence maximization can yield a constant factor approximation guarantee using polynomially-many samples, drawn from any distribution. In this paper we describe a simple algorithm for maximizing influence from training data. The main idea behind the algorithm is to leverage the strong community structure of social networks and identify a set of individuals who are influentials but whose communities have little overlap. Although in general, the approximation guarantee of such an algorithm is unbounded, we show that this algorithm performs well experimentally. To analyze its performance, we prove this algorithm obtains a constant factor approximation guarantee on graphs generated through the stochastic block model, traditionally used to model networks with community structure.
Semi-algebraic Ramsey numbers

Given a finite point set P ? R d , a k-ary semi-algebraic relation E on P is a set of k-tuples of points in P determined by a finite number of polynomial equations and inequalities in kd real variables. The description complexity of such a relation is at most t if the number of polynomials and their degrees are all bounded by t. The Ramsey number R k d , t ( s , n ) is the minimum N such that any N-element point set P in R d equipped with a k-ary semi-algebraic relation E of complexity at most t contains s members such that every k-tuple induced by them is in E or n members such that every k-tuple induced by them is not in E.We give a new upper bound for R k d , t ( s , n ) for k ? 3 and s fixed. In particular, we show that for fixed integers d , t , s R 3 d , t ( s , n ) ? 2 n o ( 1 ) , establishing a subexponential upper bound on R 3 d , t ( s , n ) . This improves the previous bound of 2 n C 1 due to Conlon, Fox, Pach, Sudakov, and Suk where C 1 depends on d and t, and improves upon the trivial bound of 2 n C 2 which can be obtained by applying classical Ramsey numbers where C 2 depends on s. As an application, we give new estimates for a recently studied Ramsey-type problem on hyperplane arrangements in R d . We also study multi-color Ramsey numbers for triangles in our semi-algebraic setting, achieving some partial results.
Reconstruction on Trees: Exponential Moment Bounds for Linear Estimators

Consider a Markov chain $(\xi_v)_{v \in V} \in [k]^V$ on the infinite $b$-ary tree $T = (V,E)$ with irreducible edge transition matrix $M$, where $b \geq 2$, $k \geq 2$ and $[k] = \{1,\ldots,k\}$. We denote by $L_n$ the level-$n$ vertices of $T$. Assume $M$ has a real second-largest (in absolute value) eigenvalue $\lambda$ with corresponding real eigenvector $\nu \neq 0$. Letting $\sigma_v = \nu_{\xi_v}$, we consider the following root-state estimator, which was introduced by Mossel and Peres (2003) in the context of the ``recontruction problem'' on trees: \begin{equation*} S_n = (b\lambda)^{-n} \sum_{x\in L_n} \sigma_x. \end{equation*} As noted by Mossel and Peres, when $b\lambda^2 > 1$ (the so-called Kesten-Stigum reconstruction phase) the quantity $S_n$ has uniformly bounded variance. Here, we give bounds on the moment-generating functions of $S_n$ and $S_n^2$ when $b\lambda^2 > 1$. Our results have implications for the inference of evolutionary trees.
Floer homology and covering spaces

We prove a Smith-type inequality for regular covering spaces in monopole Floer homology. Using the monopole Floer / Heegaard Floer correspondence, we deduce that if a 3-manifold Y admits a p^n-sheeted regular cover that is a Z/pZ-L-space (for p prime), then Y is a Z/pZ-L-space. Further, we obtain constraints on surgeries on a knot being regular covers over other surgeries on the same knot, and over surgeries on other knots.
Underdamped Langevin MCMC: A non-asymptotic analysis

We study the underdamped Langevin diffusion when the log of the target distribution is smooth and strongly concave. We present a MCMC algorithm based on its discretization and show that it achieves $\varepsilon$ error (in 2-Wasserstein distance) in $\mathcal{O}(\sqrt{d}/\varepsilon)$ steps. This is a significant improvement over the best known rate for overdamped Langevin MCMC, which is $\mathcal{O}(d/\varepsilon^2)$ steps under the same smoothness/concavity assumptions. The underdamped Langevin MCMC scheme can be viewed as a version of Hamiltonian Monte Carlo (HMC) which has been observed to outperform overdamped Langevin MCMC methods in a number of application areas. We provide quantitative rates that support this empirical wisdom.
Novel cell adhesion/migration pathways are predictive markers of HDAC inhibitor resistance in cutaneous T cell lymphoma.

Abstract Background Treatment for Cutaneous T Cell Lymphoma (CTCL) is generally not curative. Therefore, selecting therapy that is effective and tolerable is critical to clinical decision-making. Histone deacetylase inhibitors (HDACi), epigenetic modifier drugs, are commonly used but effective in only ~30% of patients. There are no predictive markers of HDACi response and the CTCL histone acetylation landscape remains unmapped. We sought to identify pre-treatment molecular markers of resistance in CTCL that progressed on HDACi therapy. Methods Purified T cells from 39 pre/post-treatment peripheral blood samples and skin biopsies from 20 patients were subjected to RNA-seq and ChIP-seq for histone acetylation marks (H3K14/9 ac, H3K27ac). We correlated significant differences in histone acetylation with gene expression in HDACi-resistant/sensitive CTCL. We extended these findings in additional CTCL patient cohorts (RNA-seq, microarray) and using ELISA in matched CTCL patient plasma. Findings Resistant CTCL exhibited high levels of histone acetylation, which correlated with increased expression of 338 genes (FDR Interpretation This study is the first to connect genome-wide differences in chromatin acetylation and gene expression to HDACi-resistance in primary CTCL. Our results identify novel markers with high pre-treatment expression, such as LAIR2, as potential prognostic and/or predictors of HDACi-resistance in CTCL. Funding NIH:CA156690, CA188286; NCATS: WU-ICTS UL1 TR000448; Siteman Cancer Center: CA091842.
Spatial Covariance Estimation for Millimeter Wave Hybrid Systems Using Out-of-Band Information

In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog–digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.
Robust Power Scheduling for Microgrids with Uncertainty in Renewable Energy Generation

Arobust power scheduling algorithm is proposed to schedule power flow between the main electricity grid and a microgird with solar energy generation and battery energy storage subject to uncertainty in solar energy production. To avoid over-conservatism in power scheduling while guaranteeing robustness against uncertainties, time-varying “soft” constraints on the State of Charge (SoC) of the battery are proposed. These soft constraints allow SoC limit violation at steps far from the current step but aim to minimize such violations in a controlled manner. The model predictive formulation of the problem over a receding time horizon ensures that the resulting solution eventually conforms to the hard SoC limits of the system at every step. The optimization problem for each step is formulated as a quadratic programming problem that is solved iteratively to find the soft constraints that are closest to the hard ones and still yield a feasible solution. Optimization results demonstrate the effectiveness of the approach.
Heuristic control of design-directed program transformations

This paper describes a class of semantic source-to-source program transformations called design-directed program transformations (DDPT) for use in a transformational implementation (TI) approach to programming. A methodology is developed for applying such transformations based on symbolic evaluation and experimental computation of programs. A DDPT is a cognitive model of source-to-source transformations; it knows what it is trying to accomplish and contains a strategy of how to accomplish it. A DDPT is more than a syntactic pattern replacement rule; it is a semantic program transformation which is intuitively closer to one that a good programmer would invoke in transforming his program.
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.
When Paywall Goes AWOL: The Demand for Open-Access Education Research:

As universities cut library funding and forego expensive journal subscriptions, many academic organizations and researchers, including the American Educational Research Association (AERA), are movi...
Algebraic Properties of Wyner Common Information Solution under Graphical Constraints

The Constrained Minimum Determinant Factor Analysis (CMDFA) setting was motivated by Wyner’s common information problem where we seek a latent representation of a given Gaussian vector distribution with the minimum mutual information under certain generative constraints. In this paper, we explore the algebraic structures of the solution space of the CMDFA, when the underlying covariance matrix Σ x has an additional latent graphical constraint, namely, a latent star topology. In particular, sufficient and necessary conditions in terms of the relationships between edge weights of the star graph have been found. Under such conditions and constraints, we have shown that the CMDFA problem has either a rank one solution or a rank n−1 solution where n is the dimension of the observable vector. Numerical results are provided to demonstrate the difference between the optimal mutual information and that derived under a naive star constraint.
The Importance of Truth Telling and Trust

Few principles influence success as fundamentally as truth. Truthfulness is the foundation upon which human relationships are built. Truth is the antecedent to trust and trust is the antecedent to cooperation. Without truth, sustainable success is impossible in human dealings. Hence, the importance of truth has been the subject of theological and scholarly pursuit for centuries. Since the latter part of the 20th century, the burgeoning fields of applied ethics has joined in this pursuit. In a 1992 essay, Stanford Business Professor Ronald A. Howard observed: “[t]he ethical dilemmas which my students and business associates seem to face evolve around issues of truth telling.” From Wells Fargo’s creation of over 2 million fake accounts, to GM’s deadly ignition switches, to the ten-billion-dollar fraud that was the healthcare and life-science company Theranos, Inc., today’s ethical dilemmas continue to evolve around issues of truth telling. How truthful we choose to be with others has a significant bearing upon reciprocal truthfulness and trust. Adherence to truthfulness and the subsequent development of trust are vital for meaningful interpersonal relationships, healthy organizational cultures, and prosperous societies. Successful leaders recognize that the organizational cost of institutionalized deceit, in both financial and human terms, is too expensive to condone. In the past seven decades, we have seen an erosion of trust in many of our institutions. Only 3% of Americans trust the federal government to do what is right “just about always” while just 14% trust the federal government to do what is right “most of the time” for a combined 17% of Americans expressing trust in the federal government. In 1958, the combined percent of Americans expressing such trust was 73%. A foundation built on a first-order principle of truth telling will better equip our students with the skills to effectively deal with the moral dilemmas that evolve around truth telling and build trust with those in their professional and personal lives.
Non-Cooperative Rational Interactive Proofs.

Interactive-proof games model the scenario where an honest party interacts with powerful but strategic provers, to elicit from them the correct answer to a computational question. Interactive proofs are increasingly used as a framework to design protocols for computation outsourcing. Existing interactive-proof games largely fall into two categories: either as games of cooperation such as multi-prover interactive proofs and cooperative rational proofs, where the provers work together as a team; or as games of conflict such as refereed games, where the provers directly compete with each other in a zero-sum game. Neither of these extremes truly capture the strategic nature of service providers in outsourcing applications. How to design and analyze non-cooperative interactive proofs is an important open problem. In this paper, we introduce a mechanism-design approach to define a multi-prover interactive-proof model in which the provers are rational and non-cooperative---they act to maximize their expected utility given others' strategies. We define a strong notion of backwards induction as our solution concept to analyze the resulting extensive-form game with imperfect information. Our protocols provide utility gap guarantees, which are analogous to soundness gap in classic interactive proofs. At a high level, a utility gap of u means that the protocol is robust against provers that may not care about a utility loss of 1/u. We fully characterize the complexity of our proof system under different utility gap guarantees. For example, we show that with a polynomial utility gap, the power of non-cooperative rational interactive proofs is exactly P^NEXP.
Back from the brink: The revitalization of inactive entrepreneurial ecosystems

Abstract Entrepreneurial ecosystems (EEs) are receiving intense attention. However, the revitalization of inactive EEs in unmunificent contexts is understudied. To investigate this phenomenon, an inductive study was conducted of a once-vibrant EE that became dormant because of an undiversified industrial base and a precipitous decline in its regional economy. The study revealed that the community members working to revitalize the city’s EE (“EE champions”) used a holistic collection of economic, socio-cultural, community, and discursive strategies. The findings have implications for scholars focused on entrepreneurship in extreme and atypical contexts, the micro-foundations of EEs, and EE leadership. The study also generates concrete insights for practitioners seeking to reinvigorate EEs.
Conservation laws and energy transformations in a class of common physics problems

We analyze a category of problems that is of interest in many physical situations, including those encountered in introductory physics classes: systems with two well-delineated parts that exchange energy, eventually reaching a shared equilibrium with a loss of mechanical or electrical energy. Such systems can be constrained by a constant of the system (e.g., mass, charge, momentum, or angular momentum) that uniquely determines the mechanical or electrical energy of the equilibrium state, regardless of the dissipation mechanism. A representative example would be a perfectly inelastic collision between two objects in one dimension, for which momentum conservation requires that some of the initial kinetic energy is dissipated by conversion to thermal or other forms as the two objects reach a common final velocity. We discuss how this feature manifests in a suite of four well-known and disparate problems that all share a common mathematical formalism. These examples, in which the energy dissipated during the process can be difficult to solve directly from dissipation rates, can be approached by students in a first-year physics class by considering conservation laws and can therefore be useful for teaching about energy transformations and conserved quantities. We then illustrate how to extend this method by applying it to a final example.
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds

We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that “training” these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at https://ai4ce.github.io/DeepMapping/.
Joint Routing and Resource Allocation for Millimeter Wave Picocellular Backhaul

Picocellular architectures are essential for providing the spatial reuse required to satisfy the ever-increasing demand for mobile data. A key deployment challenge is to provide backhaul connections with sufficiently high data rate. Providing wired support (e.g., using optical fiber) to pico base stations deployed opportunistically on lampposts and rooftops is impractical, hence wireless backhaul becomes an attractive approach. A multihop mesh network comprised of directional millimeter (mm) wave links is considered here for this purpose. Such networks are well suited for scaling backhaul data rates due to the abundance of spectrum in the mm wave bands, and the ability to form highly directional, electronically steerable beams. The backhaul design problem is formulated as one of joint routing and resource allocation, accounting for mutual interference across simultaneously active links. A computationally tractable formulation is developed by leveraging the localized nature of interference and the provable existence of a sparse optimal allocation. Numerical results are provided for topologies modeling urban and suburban settings.