cssi/SciSciGPT-SciSciNet huggingface dataset:
Opinion Dynamics with Varying Susceptibility to Persuasion

A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation on social networks: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider those that modify people's susceptibility to persuasion. Here, we adopt a popular model for social opinion dynamics, and formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility leads to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization problem and show its efficacy at finding target-sets that optimize the sum of opinions on real world networks, including a Twitter network with real opinion estimates.
Neural Network-Based Abstract Generation for Opinions and Arguments

We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.
Euclidean arrangements in Banach spaces

We study the way in which the Euclidean subspaces of a Banach space fit together, somewhat in the spirit of the Ka\v{s}in decomposition. The main tool that we introduce is an estimate regarding the convex hull of a convex body in John's position with a Euclidean ball of a given radius, which leads to a new and simplified proof of the randomized isomorphic Dvoretzky theorem. Our results also include a characterization of spaces with nontrivial cotype in terms of arrangements of Euclidean subspaces.
On Using Backpropagation for Speech Texture Generation and Voice Conversion

Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances. Similar to image texture synthesis and neural style transfer, the system works by optimizing a cost function with respect to the input waveform samples. To this end we use a differentiable mel-filterbank feature extraction pipeline and train a convolutional cTC speech recognition network. Our system is able to extract speaker characteristics from very limited amounts of target speaker data, as little as a few seconds, and can be used to generate realistic speech babble or reconstruct an utterance in a different voice.
Necessity of Future Information in Admission Control

We study the necessity of predictive information in a class of queueing admission control problems, where a system manager is allowed to divert incoming jobs up to a fixed rate, in order to minimize the queueing delay experienced by the admitted jobs. Spencer et al. (2014) [Spencer J, Sudan M, Xu K (2014) Queuing with future information. Ann. Appl. Probab. 24(5):2091–2142.] show that the system’s delay performance can be significantly improved by having access to future information in the form of a lookahead window, during which the times of future arrivals and services are revealed. They prove that, while delay under an optimal online policy diverges to infinity in the heavy-traffic regime, it can stay bounded by making use of future information. However, the diversion polices of Spencer et al. (2014) require the length of the lookahead window to grow to infinity at a nontrivial rate in the heavy-traffic regime, and it remained open whether substantial performance improvement could still be achieved with...
Analyzing multistationarity in chemical reaction networks using the determinant optimization method

Multistationary chemical reaction networks are of interest to scientists and mathematicians alike. While some criteria for multistationarity exist, obtaining explicit reaction rates and steady states that exhibit multistationarity for a given network-in order to check nondegeneracy or determine stability of the steady states, for instance-is nontrivial. Nonetheless, we accomplish this task for a certain family of sequestration networks. Additionally, our results allow us to prove the existence of nondegenerate steady states for some of these sequestration networks, thereby resolving a subcase of a conjecture of Joshi and Shiu. Our work relies on the determinant optimization method, developed by Craciun and Feinberg, for asserting that certain networks are multistationary. More precisely, we implement the construction of reaction rates and multiple steady states which appears in the proofs that underlie their method. Furthermore, we describe in detail the steps of this construction so that other researchers can more easily obtain, as we did, multistationary rates and steady states.
One-point theta functions for vertex operator algebras

Abstract One-point theta functions for modules of vertex operator algebras (VOAs) are defined and studied. These functions are a generalization of the character theta functions studied by Miyamoto and are deviations of the classical one-point functions for modules of a VOA. Transformation laws with respect to the group SL 2 ( Z ) are established.
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models

This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices, one of which is sparse, and the observations are noisy. Our main contributions come in the form of minimax lower bounds for the expected per-element squared error for this problem under several common noise models. Specifically, we analyze scenarios where the corruptions are characterized by additive Gaussian noise or additive heavier-tailed (Laplace) noise, Poisson-distributed observations, and highly-quantized (e.g., one bit) observations, as instances of our general result. Our results establish that the error bounds derived in (Soni et al ., 2016) for complexity-regularized maximum likelihood estimators achieve, up to multiplicative constants and logarithmic factors, the minimax error rates in each of these noise scenarios, provided that the nominal number of observations is large enough, and the sparse factor has (on an average) at least one non-zero per column.
Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation.

We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. We believe this advantage of the proposed deep learning model is highly desired for DR detection because in practice, users are not only interested with high prediction performance, but also keen to understand the insights of DR detection and why the adopted learning model works. In the experiments conducted on a large scale of retina image dataset, we show that the proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.
Functions of perturbed tuples of self-adjoint operators

We generalize earlier results of Aleksandrov and Peller (2010) [2,3], Aleksandrov et al. (2011) [6], Peller (1985) [13], Peller (1990) [14] to the case of functions of n-tuples of commuting self-adjoint operators. In particular, we prove that if a function f belongs to the Besov space B∞,11(Rn), then f is operator Lipschitz and we show that if f satisfies a Holder condition of order α, then ‖f(A1,…,An)−f(B1,…,Bn)‖⩽constmax1⩽j⩽n‖Aj−Bj‖α for all n-tuples of commuting self-adjoint operators (A1,…,An) and (B1,…,Bn). We also consider the case of arbitrary moduli of continuity and the case when the operators Aj−Bj belong to the Schatten–von Neumann class Sp.
A fourier-analytic approach for the discrepancy of random set systems

One of the prominent open problems in combinatorics is the discrepancy of set systems where each element lies in at most t sets. The Beck-Fiala conjecture suggests that the rightbound is [MATH HERE], but for three decades the only known bound not depending on the size of set system has been O (t). Arguably we currently lack techniques for breaking that barrier.In this paper we introduce discrepancy bounds based on Fourier analysis. We demonstrate our method on random set systems. Suppose one has n elements and m sets containing each element independently with probability p. We prove that in the regime of n ≥ Θ (m2log(m)), the discrepancy is at most 1 with high probability. Previously a result of Ezra and Lovett gave a bound of O(1) under the stricter assumption that n » mt.
DynMat, a network that can learn after learning

Abstract To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is ‘accumulative’ and ‘online’ without interruption. However, despite the recent success, artificial neural networks (ANNs) must be trained offline and suffer catastrophic interference between old and new learning, indicating that ANNs’ conventional learning algorithms may not be suitable for building intelligent agents comparable to our brain. In this study, we propose a novel neural network architecture (DynMat) consisting of dual learning systems inspired by the complementary learning system (CLS) theory suggesting that the brain relies on short- and long-term learning systems to learn continuously. Our empirical evaluations show that (1) DynMat can learn a new class without catastrophic interference and (2) it does not strictly require offline training.
Polynomial partition asymptotics

Abstract Let f ∈ Z [ y ] be a polynomial such that f ( N ) ⊆ N , and let p A f ( n ) denote number of partitions of n whose parts lie in the set A f : = { f ( n ) : n ∈ N } . Under hypotheses on the roots of f − f ( 0 ) , we use the Hardy–Littlewood circle method, a polylogarithm identity, and the Matsumoto–Weng zeta function to derive asymptotic formulae for p A f ( n ) as n tends to infinity. This generalises asymptotic formulae for the number of partitions into perfect dth powers, established by Vaughan for d = 2 , and Gafni for the case d ≥ 2 , in 2015 and 2016 respectively.
On the Christoffel Function for the Generalized Jacobi Measures on a Quasidisk

We establish the exact (up to the constants) double inequality for the Christoffel function for a measure supported on a Jordan domain bounded by a quasiconformal curve. We show that this quasiconformality of the boundary cannot be omitted.
Make up your mind: the price of online queries in differential privacy

We consider the problem of answering queries about a sensitive dataset subject to differential privacy. The queries may be chosen adversarially from a larger set Q of allowable queries in one of three ways, which we list in order from easiest to hardest to answer:• Offline: The queries are chosen all at once and the differentially private mechanism answers the queries in a single batch.• Online: The queries are chosen all at once, but the mechanism only receives the queries in a streaming fashion and must answer each query before seeing the next query.• Adaptive: The queries are chosen one at a time and the mechanism must answer each query before the next query is chosen. In particular, each query may depend on the answers given to previous queries.Many differentially private mechanisms are just as efficient in the adaptive model as they are in the offline model. Meanwhile, most lower bounds for differential privacy hold in the offline setting. This suggests that the three models may be equivalent.We prove that these models are all, in fact, distinct. Specifically, we show that there is a family of statistical queries such that exponentially more queries from this family can be answered in the offline model than in the online model. We also exhibit a family of search queries such that exponentially more queries from this family can be answered in the online model than in the adaptive model. We also investigate whether such separations might hold for simple queries like threshold queries over the real line.
Structure and overlaps of communities in networks

One of the main organizing principles in real-world social, information and technological networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. Due to unavailability of labeled ground-truth data it is practically impossible to evaluate and compare different models and notions of communities on a large scale. In this paper we identify 6 large social, collaboration, and information networks where nodes explicitly state their community memberships. We define ground-truth communities by using these explicit memberships. We then empirically study how such groundtruth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the nonoverlapping parts of communities, in contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Existing models of network communities do not capture dense community overlaps. We present the Community-Affiliation Graph Model (AGM), a conceptual model of network community structure, which reliably captures the overall structure of networks as well as the overlapping nature of network communities.
What's up with Privacy?: User Preferences and Privacy Concerns in Intelligent Personal Assistants

The recent breakthroughs in Artificial Intelligence (AI) have allowed individuals to rely on automated systems for a variety of reasons. Some of these systems are the currently popular voice-enabled systems like Echo by Amazon and Home by Google that are also called as Intelligent Personal Assistants (IPAs). Though there are rising concerns about privacy and ethical implications, users of these IPAs seem to continue using these systems. We aim to investigate to what extent users are concerned about privacy and how they are handling these concerns while using the IPAs. By utilizing the reviews posted online along with the responses to a survey, this paper provides a set of insights about the detected markers related to user interests and privacy challenges. The insights suggest that users of these systems irrespective of their concerns about privacy, are generally positive in terms of utilizing IPAs in their everyday lives. However, there is a significant percentage of users who are concerned about privacy and take further actions to address related concerns. Some percentage of users expressed that they do not have any privacy concerns but when they learned about the "always listening" feature of these devices, their concern about privacy increased.
LARGE COVARIANCE ESTIMATION THROUGH ELLIPTICAL FACTOR MODELS.

We propose a general Principal Orthogonal complEment Thresholding (POET) framework for large-scale covariance matrix estimation based on the approximate factor model. A set of high level sufficient conditions for the procedure to achieve optimal rates of convergence under different matrix norms is established to better understand how POET works. Such a framework allows us to recover existing results for sub-Gaussian data in a more transparent way that only depends on the concentration properties of the sample covariance matrix. As a new theoretical contribution, for the first time, such a framework allows us to exploit conditional sparsity covariance structure for the heavy-tailed data. In particular, for the elliptical distribution, we propose a robust estimator based on the marginal and spatial Kendall’s tau to satisfy these conditions. In addition, we study conditional graphical model under the same framework. The technical tools developed in this paper are of general interest to high dimensional principal component analysis. Thorough numerical results are also provided to back up the developed theory.
Multiple Instance Dictionary Learning using Functions of Multiple Instances

Dictionary Learning Functions of Multiple Instances (DL-FUMI) is proposed to address target detection problems with inaccurate training labels. DL-FUMI is a multiple instance dictionary learning method that estimates target atoms that describe distinctive and representative features of the target class and background atoms that account for the shared features found across both target and non-target data points. Experimental results show that the target atoms estimated by DL-FUMI are more discriminative and representative of the target class than comparison methods. DL-FUMI is shown to have improved performance on several detection problems as compared to other multiple instance dictionary learning algorithms.
Weighted growth functions of automatic groups

The growth function is the generating function for sizes of spheres around the identity in Cayley graphs of groups. We present a novel method to calculate growth functions for automatic groups with normal form recognizing automata that recognize a single normal form for each group element, and are at most context free in complexity: context free grammars can be translated into algebraic systems of equations, whose solutions represent generating functions of their corresponding non-terminal symbols. This approach allows us to seamlessly introduce weightings on the growth function: assign different or even distinct weights to each of the generators in an underlying presentation, such that this weighting is reflected in the growth function. We recover known growth functions for small braid groups, and calculate growth functions that weight each generator in an automatic presentation of the braid groups according to their lengths in braid generators.
Learning with Analytical Models

To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages. In this paper, we propose and validate a hybrid approach for performance modeling and prediction, which combines analytical and machine learning models. The proposed hybrid model aims to minimize prediction cost while providing reasonable prediction accuracy. Our validation results show that the hybrid model is able to learn and correct the analytical models to better match the actual performance. Furthermore, the proposed hybrid model improves the prediction accuracy in comparison to pure machine learning techniques while using small training datasets, thus making it suitable for hardware and workload changes.
Ridesharing with Driver Location Preferences.

We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.
Structural interpretation of thrust fault-related landforms on Mercury using Earth analogue fault models

Abstract The Yakima Fold Province has often been referenced as an Earth analogue to thrust fault-related landforms on many solar system bodies including Mars, the Moon, and Mercury. The asymmetric ridges of deformed flood basalts in this region evoke the morphology and topographic expression of the landforms observed in the Smooth Plains units of Mercury. Observed similarities have supported the interpretation that thrust faults and folds comprise both sets of landforms; however, this is an incomplete analogy. Recent modeling efforts have revealed that the terrestrial landforms developed over millions of years with vertical uplift associated with each landform accumulating on multiple faults and accommodated by large folds. These studies highlight the important role that folding plays in accommodating crustal shortening. Work presented here uses the structural geometries and folding styles from the Yakima Fold Province as a framework for constructing ten three-dimensional models of thrust fault-related landforms in Smooth Plains units near Suisei Planitia on Mercury, which traditionally would have been classified as “wrinkle ridges” based on appearance and some aspects of morphology. Forward modeling of topography revealed that multiple faults and folds are necessary to reproduce complex topographic features. Folding was found to account for >50% of vertical uplift associated with the landforms and without considering folding, shortening accommodated by the landforms was underestimated by 61 to 88%. Studies which use thrust fault-related landforms as proxies for stress and strain must therefore consider the effect of folding on these estimates and their associated effect on tectonic and thermal histories.
Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest.

Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to...
Negative curves of small genus on surfaces

Let X be a smooth geometrically irreducible projective surface over a field. In this paper we give an effective upper bound in terms of the Neron–Severi rank of X for the number of irreducible curves C on X with negative self-intersection and geometric genus less than $$b_1(X)/4$$, where $$b_1(X)$$ is the first etale Betti number of X. The proof involves a hyperbolic analog of the theory of spherical codes. More specifically, we relate these curves to the hyperbolic kissing number, and then prove upper and lower bounds for the hyperbolic kissing number in terms of the classical Euclidean kissing number.
Exploring perceptual illusions in deep neural networks

Abstract Perceptual illusions—discrepancies between what exists externally and what we actually see—reveal a great deal about how the perceptual system functions. Rather than failures of perception, illusions expose automatic computations and biases in visual processing that help make better decisions from visual information to achieve our perceptual goals. Recognizing objects is one such perceptual goal that is shared between humans and certain Deep Convolutional Neural Networks, which can reach human-level performance. Do neural networks trained exclusively for object recognition “perceive” visual illusions, simply as a result of solving this one perceptual problem? Here, I showed four classic illusions to humans and a pre-trained neural network to see if the network exhibits similar perceptual biases. I found that deep neural networks trained exclusively for object recognition exhibit the Muller-Lyer illusion, but not other illusions. This result shows that some perceptual computations that are similar to humans’ may come “for free” in a system with perceptual goals similar to humans’.
Deep multimodality model for multi-task multi-view learning

Many real-world problems exhibit the coexistence of multiple types of heterogeneity, such as view heterogeneity (i.e., multi-view property) and task heterogeneity (i.e., multi-task property). For example, in an image classification problem containing multiple poses of the same object, each pose can be considered as one view, and the detection of each type of object can be treated as one task. Furthermore, in some problems, the data type of multiple views might be different. In a web classification problem, for instance, we might be provided an image and text mixed data set, where the web pages are characterized by both images and texts. A common strategy to solve this kind of problem is to leverage the consistency of views and the relatedness of tasks to build the prediction model. In the context of deep neural network, multi-task relatedness is usually realized by grouping tasks at each layer, while multi-view consistency is usually enforced by finding the maximal correlation coefficient between views. However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.). In this paper, we bridge this gap by proposing a deep multi-task multi-view learning framework that learns a deep representation for such dual-heterogeneity problems. Empirical studies on multiple real-world data sets demonstrate the effectiveness of our proposed Deep-MTMV algorithm.
The importance of designing a comprehensive Strategic Environmental Assessment (SEA) & Environmental Impact Assessment (EIA) for carbon capture and storage in Japan

Abstract CCS projects involve a complex legal mechanism. The IPCC (2005) clearly indicated that installations of CCS technology require ‘long-term management’ by: ensuring careful site selection; monitoring systems; regulations; and remediation and liability measures. While a large volume of legal study on CCS has been undertaken, a variety of SEA and EIA and their possible mixed approaches are also being considered for designing an effective policy and legal framework for CCS (e.g. IEAGHG, 2007; and Koornneef et al. 2008, 2011). Yet, the best practice of the mixed approach to assisting a policy, legal and regulatory framework is unclear, due to the lack of experience and the varied levels of comprehension. Japan intends to commercialise CCS technology in the future. This paper provides an analysis of examining: whether the key roles and elements of SEA and EIA in response to long-term management are being covered in the current CCS law in Japan. The outcome of this study creates a novel framework of legal analysis, an application of the framework that articulate insights into Japan’s legal issues, and a set of potentially useful lessons for the future design of policy and legal framework for CCS projects.
Conceptual Knowledge of Confidence Intervals in Psychology Undergraduate and Graduate Students.

In recent years, there have been calls for researchers to report and interpret confidence intervals (CIs) rather than relying solely on p-values. Such reforms, however, may be hindered by a general lack of understanding of CIs and how to interpret them. In this study, we assessed conceptual knowledge of CIs in undergraduate and graduate psychology students. CIs were difficult and prone to misconceptions for both groups. Connecting CIs to estimation and sample mean concepts was associated with greater conceptual knowledge of CIs. Connecting CIs to null hypothesis significance testing, however, was not associated with conceptual knowledge of CIs. It may therefore be beneficial to focus on estimation and sample mean concepts in instruction about CIs. First published May 2019 at Statistics Education Research Journal Archives
Divergent behavioral responses in protracted opioid withdrawal in male and female C57BL/6J mice

Persons suffering from opioid use disorder (OUD) experience long-lasting dysphoric symptoms well into extended periods of withdrawal. This protracted withdrawal syndrome is notably characterized by heightened anxiety and hyperkatifeia. Here we investigated if an exacerbated withdrawal model of acute morphine dependence results in lasting behavioral adaptation 6 weeks into forced abstinence in C57BL/6J mice. We found that our exacerbated morphine withdrawal paradigm produced distinct alterations in behavior in elevated-plus maze (EPM), open field, and social interaction tests in male and female mice. Following protracted withdrawal male mice showed enhanced exploration of the open arms of the EPM, reduced latency to enter the corner of the OF, and a social interaction deficit. In contrast, female mice showed enhanced thigmotaxis in the OF. Both sexes showed that protracted withdrawal enhanced locomotor behavior in response to subsequent morphine challenge, albeit at different doses. These findings will be relevant for future investigation examining the neural mechanisms underlying these behaviors, and will aid in uncovering physiological sex differences in response to opioid withdrawal.
Reciprocal space imaging of ionic correlations in intercalation compounds

The intercalation of alkali ions into layered materials has played an essential role in battery technology since the development of the first lithium-ion electrodes. Coulomb repulsion between the intercalants leads to ordering of the intercalant sublattice, which hinders ionic diffusion and impacts battery performance. While conventional diffraction can identify the long-range order that can occur at discrete intercalant concentrations during the charging cycle, it cannot determine short-range order at other concentrations that also disrupt ionic mobility. In this Article, we show that the use of real-space transforms of single-crystal diffuse scattering, measured with high-energy synchrotron X-rays, allows a model-independent measurement of the temperature dependence of the length scale of ionic correlations along each of the crystallographic axes in sodium-intercalated V2O5. The techniques described here provide a new way of probing the evolution of structural ordering in crystalline materials. Conventional diffraction cannot determine short-range order at concentrations that disrupt ionic mobility. Real-space transforms of single-crystal diffuse scattering now allow us to measure ionic correlation length scales in sodium-intercalated V2O5.
Periodic Reeb flows and products in symplectic homology

In this paper, we explore the structure of Rabinowitz--Floer homology $RFH_*$ on contact manifolds whose Reeb flow is periodic (and which satisfy an index condition such that $RFH_*$ is independent of the filling). The main result is that $RFH_*$ is a module over the Laurent polynomials $\mathbb{Z}_2[s,s^{-1}]$, where $s$ is the homology class generated by a principal Reeb orbit and the module structure is given by the pair-of-pants product. In most cases, this module is free and finitely generated.
Performance Bounds and Estimates for Quantized LDPC Decoders

The performance of low-density parity-check (LDPC) codes at high signal-to-noise ratios (SNRs) is known to be limited by the presence of certain sub-graphs that exist in the Tanner graph representation of the code, for example trapping sets and absorbing sets. This paper derives a lower bound on the frame error rate (FER) of any LDPC code containing a given problematic sub-graph, assuming a particular message passing decoder and decoder quantization. A crucial aspect of the lower bound is that it is code-independent, in the sense that it can be derived based only on a problematic sub-graph and then applied to any code containing it. Due to the complexity of evaluating the exact bound, assumptions are proposed to approximate it, from which we can estimate decoder performance. Simulated results obtained for both the quantized sum-product algorithm (SPA) and the quantized min-sum algorithm (MSA) are shown to be consistent with the approximate bound and the corresponding performance estimates. Different classes of LDPC codes, including both structured and randomly constructed codes, are used to demonstrate the robustness of the approach.
Expanding Polynomials on Sets with Few Products

In this note, we prove that if A is a finite set of real numbers such that |AA|=K|A|, then for every polynomial f∈R[x,y] we have that |f(A,A)|=Ω_(K,degf)(|A|²), unless f is of the form f(x,y)=g(M(x,y)) for some monomial M and some univariate polynomial g. This is sharp up to the dependence on K and the degree of f.
A stable cardinality distance for topological classification

This work incorporates topological features via persistence diagrams to classify point cloud data arising from materials science. Persistence diagrams are multisets summarizing the connectedness and holes of given data. A new distance on the space of persistence diagrams generates relevant input features for a classification algorithm for materials science data. This distance measures the similarity of persistence diagrams using the cost of matching points and a regularization term corresponding to cardinality differences between diagrams. Establishing stability properties of this distance provides theoretical justification for the use of the distance in comparisons of such diagrams. The classification scheme succeeds in determining the crystal structure of materials on noisy and sparse data retrieved from synthetic atom probe tomography experiments.
The unusual classical Cepheid HR 7308: 1966–1969

The small-amplitude classical cepheid HR 7308 has the shortest period known in our Galaxy for a classical cepheid. 1966–1969 photoelectric observations together with more recent data show a period of 1 $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle\cdot}$}}{d}$$ 49078 with sinusoidal light variations. Except for the presence of the beat period, no secondary period can be found in the extensive 1966 data. The lack of a secondary period near p1/p0 = 0.7 sets HR 7308 apart from many other short-period classical cepheids. The amplitude of HR 7308 varies an a timescale near 970 days. The variations may be periodic. The available data do not at present allow us to distinguish between the Blazhko Effect (modulation of a regular variation) and a model with two very close interfering periods in HR 7308.
The Herpes Simplex Virus 1 Immediate Early Protein ICP22 Is a Functional Mimic of a Cellular J Protein.

Molecular chaperones and cochaperones are the most abundant cellular effectors of protein homeostasis, assisting protein folding and preventing aggregation of misfolded proteins. We have previously shown that herpes simplex virus 1 (HSV-1) infection results in the drastic spatial reorganization of the cellular chaperone Hsc70 into nuclear domains called VICE ( V irus I nduced C haperone E nriched) domains and that this recruitment is dependent on the viral immediate early protein ICP22. Here, we present several lines of evidence supporting the notion that ICP22 functions as a virally encoded cochaperone (J-protein/Hsp40) functioning together with its Hsc70 partner to recognize and manage aggregated and misfolded proteins. We show that ICP22 results in (i) nuclear sequestration of nonnative proteins, (ii) reduction of cytoplasmic aggresomes in cells expressing aggregation-prone proteins, and (iii) thermoprotection against heat inactivation of firefly luciferase, and (iv) sequence homology analysis indicated that ICP22 contains an N-terminal J domain and a C-terminal substrate binding domain, similar to type II cellular J proteins. ICP22 may thus be functionally similar to J-protein/Hsp40 cochaperones that function together with their HSP70 partners to prevent aggregation of nonnative proteins. This is not the first example of a virus hijacking a function of a cellular chaperone, since simian immunodeficiency virus T antigen was previously shown to contain a J domain; however, this the first known example of the acquisition of a functional J-like protein by a virus and suggests that HSV has taken advantage of the adaptable nature of J proteins to evolve a multifunctional cochaperone that functions with Hsc70 to promote lytic infection.IMPORTANCE Viruses have evolved a variety of strategies to succeed in a hostile environment. The herpes simplex virus 1 (HSV-1) immediate early protein ICP22 plays several roles in the virus life cycle, including downregulation of cellular gene expression, upregulation of late viral gene expression, inhibition of apoptosis, prevention of aggregation of nonnative proteins, and the recruitment of a cellular heat shock protein, Hsc70, to nuclear domains. We present evidence that ICP22 functionally resembles a cellular J-protein/HSP40 family cochaperone, interacting specifically with Hsc70. We suggest that HSV has taken advantage of the adaptable nature of J proteins to evolve a multifunctional cochaperone that functions with Hsc70 to promote lytic infection.
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite the increasing interest, the key challenge is to construct proper representations of molecules for learning algorithms. This paper introduces the N-gram graph, a simple unsupervised representation for molecules. The method first embeds the vertices in the molecule graph. It then constructs a compact representation for the graph by assembling the vertex embeddings in short walks in the graph, which we show is equivalent to a simple graph neural network that needs no training. The representations can thus be efficiently computed and then used with supervised learning methods for prediction. Experiments on 60 tasks from 10 benchmark datasets demonstrate its advantages over both popular graph neural networks and traditional representation methods. This is complemented by theoretical analysis showing its strong representation and prediction power.
Higher-order accurate diffuse-domain methods for partial differential equations with Dirichlet boundary conditions in complex, evolving geometries

Abstract The diffuse-domain, or smoothed boundary, method is an attractive approach for solving partial differential equations in complex geometries because of its simplicity and flexibility. In this method the complex geometry is embedded into a larger, regular domain. The original PDE is reformulated using a smoothed characteristic function of the complex domain and source terms are introduced to approximate the boundary conditions. The reformulated equation, which is independent of the dimension and domain geometry, can be solved by standard numerical methods and the same solver can be used for any domain geometry. A challenge is making the method higher-order accurate. For Dirichlet boundary conditions, which we focus on here, current implementations demonstrate a wide range in their accuracy but generally the methods yield at best first order accuracy in ϵ, the parameter that characterizes the width of the region over which the characteristic function is smoothed. Typically, ϵ ∝ h , the grid size. Here, we analyze the diffuse-domain PDEs using matched asymptotic expansions and explain the observed behaviors. Our analysis also identifies simple modifications to the diffuse-domain PDEs that yield higher-order accuracy in ϵ, e.g., O ( ϵ 2 ) in the L 2 norm and O ( ϵ p ) with 1.5 ≤ p ≤ 2 in the L ∞ norm. Our analytic results are confirmed numerically in stationary and moving domains where the level set method is used to capture the dynamics of the domain boundary and to construct the smoothed characteristic function.
Combining Matching and Synthetic Control to Trade oﬀ Biases from Extrapolation and Interpolation

The synthetic control method is widely used in comparative case studies to adjust for diﬀerences in pre-treatment characteristics. A major attraction of the method is that it limits extrapolation bias that can occur when untreated units with diﬀerent pre-treatment characteristics are combined using a traditional adjustment, such as a linear regression. Instead, the SC estimator is susceptible to interpolation bias because it uses a convex weighted average of the untreated units to create a synthetic untreated unit with pre-treatment characteristics similar to those of the treated unit. More traditional matching estimators exhibit the opposite behavior: they limit interpolation bias at the potential expense of extrapolation bias. We propose combining the matching and synthetic control estimators through model averaging to create an estimator called MASC. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve trade-oﬀs between interpolation and extrapolation bias. We use a series of empirically-based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we use the MASC re-examine the economic costs of conﬂicts and ﬁnd evidence of larger eﬀects than with SC.
Tunable perfect absorption in a subwavelength metallic grating-coupled graphene plasmonic meta-surface

Electronically tunable perfect absorption in subwavelength-metallic-grating-coupled graphene plasmonic is proposed for the near-infrared to visible spectrum range. Near perfect absorption is reported in graphene plasmonics with maximum absorption of 99.5% at 410 THz.
An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries.

Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naive user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.
Wave excitation force estimation of wave energy floats using extended Kalman filters

Abstract In many advanced control strategies the wave excitation force is key to determining the control input. However, it is often difficult to measure the excitation force on a Wave Energy Converter (WEC). The use of Kalman filters to estimate the wave excitation force based on readily available measurement data can potentially fill the gap between the development of WEC control strategies and the data that is available. Two different estimation methods using an nonlinear Extended Kalman Filter are tested on experimental wave tank data for a heaving semi-submerged float. The first method relies on directly including the excitation force as a state in the first order dynamics—which allows the “random walk” of the Kalman filter to identify an estimate of the excitation force. The second method of estimation involves modeling the wave excitation force as a harmonic oscillator comprised of sinusoidal components. Both methods are evaluated for a variety of incident waves and additional sensitivity analyses are performed to investigate the susceptibility of these estimation methods to changes in the model, measurement noise, and sampling rate.
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multi-fingered robotic grasping remains a challenge due to large sim-to-real fidelity gaps and the high sample complexity of on-policy RL algorithms. This work aims to bridge these gaps by first reinforcement-learning a multi-fingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Using a mapping from pixel space to Cartesian space according to the depth map, this method transfers to the real world with high fidelity and introduces a novel attention mechanism that substantially improves grasp success rate in cluttered environments. Finally, the direct-generative nature of this method allows learning of multi-fingered grasps that have flexible end-effector positions, orientations and rotations, as well as all degrees of freedom of the hand.
Fundamental Limits of Quantum-Secure Covert Communication Over Bosonic Channels

We investigate the fundamental limit of quantum-secure covert communication over the lossy thermal noise bosonic channel, the quantum-mechanical model underlying many practical channels. We assume that the adversary has unlimited quantum information processing capabilities as well as access to all transmitted photons that do not reach the legitimate receiver. Given existence of noise that is uncontrolled by the adversary, the square root law (SRL) governs covert communication: up to $c\sqrt {n}$ covert bits can be transmitted reliably in $n$ channel uses. Attempting to surpass this limit results in detection with unity probability as $n\rightarrow \infty $ . Here we present the expression for $c$ , characterizing the SRL for the bosonic channel. We also prove that discrete-valued coherent state quadrature phase shift keying (QPSK) constellation achieves the optimal $c$ , which is the same as that achieved by a circularly-symmetric complex-valued Gaussian prior on coherent state amplitude. Finally, while binary phase shift keying (BPSK) achieves the Holevo capacity for non-covert bosonic channels in the low received signal-to-noise ratio regime, we show that it is strictly sub-optimal for covert communication.