cssi/SciSciGPT-SciSciNet huggingface dataset:
Averaging one-point hyperbolic-type metrics

It is known that the $\tilde j$-metric, half-apollonian metric and scale-invariant Cassinian metric are not Gromov hyperbolic. These metrics are defined as a supremum of one-point metrics (i.e., metrics constructed using one boundary point) and the supremum is taken over all boundary points. The aim of this paper is to show that taking the average instead of the supremum yields a metric that preserves the Gromov hyperbolicity. Moreover, we show that the Gromov hyperbolicity constant of the resulting metric does not depend on the number of metrics used in taking the average. We also provide an example to show that the average of Gromov hyperbolic metrics is not, in general, Gromov hyperbolic.
Multi-Parameter Tikhonov Regularization

We study multi-parameter Tikhonov regularization, i.e., with multiple penalties. Such models are useful when the sought-for solution exhibits several distinct features simultaneously. Two choice rules, i.e., discrepancy principle and balancing principle, are studied for choosing an appropriate (vector-valued) regularization parameter, and some theoretical results are presented. In particular, the consistency of the discrepancy principle as well as convergence rate are established, and an a posteriori error estimate for the balancing principle is established. Also two fixed point algorithms are proposed for computing the regularization parameter by the latter rule. Numerical results for several nonsmooth multi-parameter models are presented, which show clearly their superior performance over their single-parameter counterparts.
Null spaces of Radon transforms

We obtain new descriptions of the null spaces of sev- eral projectively equivalent transforms in integral geometry. The paper deals with the hyperplane Radon transform, the totally ge- odesic transforms on the sphere and the hyperbolic space, the spherical slice transform, and the Cormack-Quinto spherical mean transform for spheres through the origin. The consideration ex- tends to the corresponding dual transforms and the relevant exte- rior/interior modifications. The method relies on new results for the Gegenbauer-Chebyshev integrals, which generalize Abel type fractional integrals on the positive half-line.
From nanoscale to macroscale: applications of nanotechnology to production of bulk ultra-strong materials

Carbon nanotubes have been famous since their discovery twenty years ago for their remarkable physical properties, from strength a hundred times higher than steel, to electrical current capacity a 1,000 times higher than copper. But so far they have only been produced at most up to centimeter lengths. Here are presented some proposals to combine the nanotubes in such a way to get arbitrarily long lengths while maintaining their extraordinary physical properties.
An Entropy Inequality for Symmetric Random Variables

We establish a lower bound on the entropy of weighted sums of (possibly dependent) random variables (X 1 , X 2 , …, X n) possessing a symmetric joint distribution. Our lower bound is in terms of the joint entropy of (X 1 , X 2 , …, Xn). We show that for $n$ ≥ 3, the lower bound is tight if and only if X i 'S are i.i.d. Gaussian random variables. For $n$ = 2 there are numerous other cases of equality apart from i.i.d. Gaussians, which we completely characterize. Going beyond sums, we also present an inequality for certain linear transformations of (X 1 , …, X n.). Our primary technical contribution lies in the analysis of the equality cases, and our approach relies on the geometry and the symmetry of the problem.
Divisibility Tests Unified: Stacking the Trimmings for Sums

SummaryDivisibility tests are algorithms that can quickly decide if one integer is divisible by another. There are many tests but most are either of the trimming or summing variety. Our goals are t...
Generating sets for coordinate rings of character varieties

We find finite, reasonably small, generator sets of the coordinate rings of G-character varieties of finitely generated groups for all classical matrix groups G. This result together with the method of Grobner basis gives an algorithm for describing character varieties by explicit polynomial equations. Additionally, we describe finite sets of generators of the fields of rational functions on G-character varieties for all exceptional algebraic groups G.
Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards

We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and random censoring within strata of treatment and baseline variables, the proposed estimator has the following features: (i) it is interpretable under violations of the proportional hazards assumption; (ii) it is consistent and at least as precise as the Kaplan-Meier and inverse probability weighted estimators, under identifiability conditions; (iii) it remains consistent under violations of independent censoring (unlike the Kaplan-Meier estimator) when either the censoring or survival distributions, conditional on covariates, are estimated consistently; and (iv) it achieves the nonparametric efficiency bound when both of these distributions are consistently estimated. We illustrate the performance of our method using simulations based on resampling data from a completed, phase 3 randomized clinical trial of a new surgical treatment for stroke; the proposed estimator achieves a 12% gain in relative efficiency compared to the Kaplan-Meier estimator. The proposed estimator has potential advantages over existing approaches for randomized trials with time-to-event outcomes, since existing methods either rely on model assumptions that are untenable in many applications, or lack some of the efficiency and consistency properties (i)-(iv). We focus on estimation of the restricted mean survival time, but our methods may be adapted to estimate any treatment effect measure defined as a smooth contrast between the survival curves for each study arm. We provide R code to implement the estimator.
Are GANs Created Equal? A Large-Scale Study

Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in \cite{goodfellow2014generative}.
Entropy Dissipation Semi-Discretization Schemes for Fokker–Planck Equations

We propose a new semi-discretization scheme to approximate nonlinear Fokker–Planck equations, by exploiting the gradient flow structures with respect to the 2-Wasserstein metric in the space of probability densities. We discretize the underlying state by a finite graph and define a discrete 2-Wasserstein metric in the discrete probability space. Based on such metric, we introduce a gradient flow of the discrete free energy as semi discretization scheme. We prove that the scheme maintains dissipativity of the free energy and converges to a discrete Gibbs measure at exponential dissipation rate. We exhibit these properties on several numerical examples.
The A-polynomial and holonomy perturbations

Dunfield-Garoufalidis and Boyer-Zhang proved that the A-polynomial of a nontrivial knot in $S^{3}$ is nontrivial. In this paper, we use holonomy perturbations to prove the non-triviality of the A-polynomial for a nontrivial, null-homotopic knot in an irreducible 3-manifold. Also, we give a strong constraint on the A-polynomial of a knot in the 3-sphere.
Generic extensions and generic polynomials for linear algebraic groups

Abstract We construct generic extensions and polynomials for certain linear algebraic groups over finite fields. In particular, we show the existence of generic polynomials for the cyclic group C 2 m in characteristic p for all odd primes p.
INTEGER HULLS OF LINEAR POLYHEDRA AND SCL IN FAMILIES

The integer hull of a polyhedron is the convex hull of the integer points contained in it. We show that the vertices of the integer hulls of a rational family of polyhedra of size O(n) have eventually quasipolynomial coordinates. As a corollary, we show that the stable commutator length of elements in a surgery family is eventually a ratio of quasipolynomials, and that unit balls in the scl norm eventually quasiconverge in finite-dimensional surgery families.
Any Size for a Dollar: The Effect of Any‐Size‐Same‐Price versus Standard Pricing on Beverage Size Choices

Overconsumption of sugary drinks is linked with obesity. These beverages have been the target of recent public health policies, and, simultaneously, some firms have altered their pricing of such beverages, including charging the same price for all beverage sizes (“any-size-same-price” pricing). We compare the effect of any-size-same-price pricing versus standard pricing on soft drink beverage size choices and further, explore the effect of this firm lever under different health-related policy situations. Overall, we show that any-size-same-price pricing increases consumers’ focus on the value of getting a good financial deal and thereby increases beverage size selections. Further, the allure of any-size-same-price pricing prevents calorie postings from successfully reducing choice of larger sizes as occurs under standard pricing. However, a more graphic health intervention can reduce the appeal of larger sizes under any-size-same-price pricing. Finally, the findings are not moderated by diet versus non-diet beverage selections, indicating that consumers do not perceive the value of larger sizes under any-size-same-price pricing to come from getting more calories. We conclude by discussing how this work can improve the design of public health nutrition policies.
Online learning with feedback graphs and switching costs

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous works studied the expected regret of the learner in the case of a clique (Expert setup), or disconnected single loops (Multi-Armed Bandits (MAB)). This work provides a lower bound on the expected regret in the Partial Information (PI) setting, namely for general feedback graphs --excluding the clique. Additionally, it shows that all algorithms that are optimal without switching costs are necessarily sub-optimal in the presence of switching costs, which motivates the need to design new algorithms. We propose two new algorithms: Threshold Based EXP3 and EXP3.SC. For the two special cases of symmetric PI setting and MAB, the expected regret of both of these algorithms is order optimal in the duration of the learning process. Additionally, Threshold Based EXP3 is order optimal in the switching cost, whereas EXP3.SC is not. Finally, empirical evaluations show that Threshold Based EXP3 outperforms the previously proposed order-optimal algorithms EXP3 SET in the presence of switching costs, and Batch EXP3 in the MAB setting with switching costs.
Boundary Flow: A Siamese Network that Predicts Boundary Motion Without Training on Motion

Using deep learning, this paper addresses the problem of joint object boundary detection and boundary motion estimation in videos, which we named boundary flow estimation. Boundary flow is an important mid-level visual cue as boundaries characterize objects' spatial extents, and the flow indicates objects' motions and interactions. Yet, most prior work on motion estimation has focused on dense object motion or feature points that may not necessarily reside on boundaries. For boundary flow estimation, we specify a new fully convolutional Siamese network (FCSN) that jointly estimates object-level boundaries in two consecutive frames. Boundary correspondences in the two frames are predicted by the same FCSN with a new, unconventional deconvolution approach. Finally, the boundary flow estimate is improved with an edgelet-based filtering. Evaluation is conducted on three tasks: boundary detection in videos, boundary flow estimation, and optical flow estimation. On boundary detection, we achieve the state-of-the-art performance on the benchmark VSB100 dataset. On boundary flow estimation, we present the first results on the Sintel training dataset. For optical flow estimation, we run the recent approach CPM-Flow but on the augmented input with our boundary-flow matches, and achieve significant performance improvement on the Sintel benchmark.
Normal smoothings for Charney–Davis strict hyperbolizations

We prove that the Charney–Davis Strict Hyperbolization of a smoothly cubulated manifold admits a normal smooth structure. We also prove that this normal smooth structure is diffeomorphic to a smooth structure that has good tangential properties. The results in this paper are key ingredients in problem of smoothing the metric of a strictly hyperbolized manifold (see [3, 8]).
Spectral echolocation via the wave embedding

Abstract Spectral embedding uses eigenfunctions of the discrete Laplacian on a weighted graph to obtain coordinates for an embedding of an abstract data set into Euclidean space. We propose a new pre-processing step of first using the eigenfunctions to simulate a low-frequency wave moving over the data and using both position as well as change in time of the wave to obtain a refined metric to which classical methods of dimensionality reduction can then applied. This is motivated by the behavior of waves, symmetries of the wave equation and the hunting technique of bats. It is shown to be effective in practice and also works for other partial differential equations – the method yields improved results even for the classical heat equation.
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%.
Random walks on quasisymmetric functions

Abstract Conditions are provided under which an endomorphism on quasisymmetric functions gives rise to a left random walk on the descent algebra which is also a lumping of a left random walk on permutations. Spectral results are also obtained. Several important random walks are now realized this way: Stanley's QS -distribution results from endomorphisms given by evaluation maps, a -shuffles result from the a th convolution power of the universal character, and the Tchebyshev operator of the second kind introduced recently by Ehrenborg and Readdy yields traditional riffle shuffles. A conjecture of Ehrenborg regarding the spectra for a family of random walks on ab -words is proven. A theorem of Stembridge from the theory of enriched P -partitions is also recovered as a special case.
Lower Bounds for Higher-Order Convex Optimization

State-of-the-art methods in convex and non-convex optimization employ higher-order derivative information, either implicitly or explicitly. We explore the limitations of higher-order optimization and prove that even for convex optimization, a polynomial dependence on the approximation guarantee and higher-order smoothness parameters is necessary. As a special case, we show Nesterov's accelerated cubic regularization method to be nearly tight.
Autoduality holds for a degenerating abelian variety

We prove that certain degenerate abelian varieties that include compactified Jacobians, namely stable semiabelic varieties, satisfy autoduality. We establish this result by proving a comparison theorem that relates the associated family of Picard schemes to the Neron model, a result of independent interest. In our proof, a key fact is that the total space of a suitable family of stable semiabelic varieties has rational singularities.
A nonparametric framework for quantifying generative inference on neuromorphic systems

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in probabilistic generative model applications such as image occlusion removal, pattern completion and motion synthesis. Generative inference in such algorithms can be performed very efficiently on hardware using a Markov Chain Monte Carlo procedure called Gibbs sampling, where stochastic samples are drawn from noisy integrate and fire neuron s implemented on neuromorphic substrates. Currently, no satisfactory metrics exist for evaluating the generative performance of such algorithms implemented on high-dimensional data for neuromorphic platforms. This paper demonstrates the application of nonparametric goodness-of-fit testing to both quantify the generative performance as well as provide decision-directed criteria for choosing the parameters of the neuromorphic Gibbs sampler and optimizing usage of hardware resources used during sampling.
Non-separable, quasiconcave utilities are easy in a perfect price discrimination market model

Recent results, establishing evidence of intractability for such restrictive utility functions as additively separable, piecewise-linear and concave, under both Fisher and Arrow-Debreu market models, have prompted the question of whether we have failed to capture some essential elements of real markets, which seem to do a good job of finding prices that maintain parity between supply and demand. The main point of this paper is to show that even non-separable, quasiconcave utility functions can be handled efficiently in a suitably chosen, though natural, realistic and useful, market model; our model allows for perfect price discrimination. Our model supports unique equilibrium prices and, for the restriction to concave utilities, satisfies both welfare theorems.
Explicit left orders on free groups extending the lexicographic order on free monoids

Abstract For every finitely generated free group, we construct an explicit left order extending the lexicographic order on the free monoid generated by the positive letters. The order is defined by a left, free action on the orbit of 0 of a free group of piecewise linear homeomorphisms of the line. The membership in the positive cone is decidable in linear time in the length of the input word. The positive cone forms a context-free language closed under word reversal.
Elliptic elements in a Weyl group: a homogeneity property

Let G be a reductive group over an algebraically closed field whose characteristic is not a bad prime for G. Let w be an elliptic element of the Weyl group which has minimal length in its conjugacy class. We show that there exists a unique unipotent class X in G such that the following holds: if V is the variety of pairs consisting of an element g in X and a Borel subgroup B such that B,gBg^{-1} are in relative position w, then V is a homogeneous G-space.
Elementary Deuring-Heilbronn Phenomenon

Adapting a technique of Pintz, we give an elementary demonstration of the Deuring phenomenon: a zero of \zeta(s) off the critical line gives a lower bound on L(1,\chi). The necessary tools are Dirichlet's 'method of the hyperbola', Euler summation, summation by parts, and the Polya-Vinogradov inequality.
Copper blocks V-ATPase activity and SNARE complex formation to inhibit yeast vacuole fusion

The accumulation of copper in organisms can lead to altered functions of various pathways and become cytotoxic through the generation of reactive oxygen species. In yeast, cytotoxic metals such as Hg+ , Cd2+ and Cu2+ are transported into the lumen of the vacuole through various pumps. Copper ions are initially transported into the cell by the copper transporter Ctr1 at the plasma membrane and sequestered by chaperones and other factors to prevent cellular damage by free cations. Excess copper ions can subsequently be transported into the vacuole lumen by an unknown mechanism. Transport across membranes requires the reduction of Cu2+ to Cu+ . Labile copper ions can interact with membranes to alter fluidity, lateral phase separation and fusion. Here we found that CuCl2 potently inhibited vacuole fusion by blocking SNARE pairing. This was accompanied by the inhibition of V-ATPase H+ pumping. Deletion of the vacuolar reductase Fre6 had no effect on the inhibition of fusion by copper. This suggests that Cu2+ is responsible for the inhibition of vacuole fusion and V-ATPase function. This notion is supported by the differential effects of chelators. The Cu2+ -specific chelator triethylenetetramine rescued fusion, whereas the Cu+ -specific chelator bathocuproine disulfonate had no effect on the inhibited fusion.
Quantifying Behavioral Sensation Seeking With the Aroma Choice Task

Our goal was to develop a behavioral measure of sensation seeking (SS). The Aroma Choice Task (ACT) assesses preference for an intense, novel, varied, and risky (exciting) option versus a mild, safe (boring) option using real-time odorant delivery. A total of 147 healthy young adults completed 40 binary choice trials. We examined (1) intensity and pleasantness of odorants, (2) stability of responding, (3) association with SS self-report, and (4) association with self-reported illicit drug use. Participants' preference for the "exciting" option versus the safe option was significantly associated with self-reported SS (p < .001) and illicit drug use (p = .041). Odorant ratings comported with their intended intensity. The ACT showed good internal, convergent, and criterion validity. We propose that the ACT might permit more objective SS assessment for investigating the biological bases of psychiatric conditions marked by high SS, particularly addiction. The ACT measures SS behaviorally, mitigating some self-report challenges and enabling real-time assessment, for example, for functional magnetic resonance imaging (fMRI).
The Distinction Between Fixed and Random Generators in Group-Based Assumptions

There is surprisingly little consensus on the precise role of the generator g in group-based assumptions such as DDH. Some works consider g to be a fixed part of the group description, while others take it to be random. We study this subtle distinction from a number of angles.
Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

Self-organizing networks (SONs) can help to manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on the multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we introduce Q-learning-based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables the ongoing transmit power adaptation as new small cells are added to the network. Furthermore, the sample complexity of the Q-DPA algorithm to achieve $\epsilon $ -optimality with high probability is provided. We demonstrate, at the density of several thousands femtocells per km2, the required quality of service of a macrocell user can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.
Measuring Collaborative Emergent Behavior in Multi-agent Reinforcement Learning.

Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee of the collaborative behavior thought to be important for solving multi-agent tasks. To address this, we present a novel approach for quantitatively assessing collaboration in continuous spatial tasks with multi-agent RL. Such a metric is useful for measuring collaboration between computational agents and may serve as a training signal for collaboration in future RL paradigms involving humans.
On the Computational Complexity of Minimal Cumulative Cost Graph Pebbling

We consider the computational complexity of finding a legal black pebbling of a DAG \(G=(V,E)\) with minimum cumulative cost. A black pebbling is a sequence \(P_0,\ldots , P_t \subseteq V\) of sets of nodes which must satisfy the following properties: \(P_0 = \emptyset \) (we start off with no pebbles on G), \({\mathsf {sinks}} (G) \subseteq \bigcup _{j \le t} P_j\) (every sink node was pebbled at some point) and \({\mathsf {parents}} \big (P_{i+1}\backslash P_i\big ) \subseteq P_i\) (we can only place a new pebble on a node v if all of v’s parents had a pebble during the last round). The cumulative cost of a pebbling \(P_0,P_1,\ldots , P_t \subseteq V\) is \({\mathsf {cc}} (P) = \left| P_1\right| + \ldots + \left| P_t\right| \). The cumulative pebbling cost is an especially important security metric for data-independent memory hard functions, an important primitive for password hashing. Thus, an efficient (approximation) algorithm would be an invaluable tool for the cryptanalysis of password hash functions as it would provide an automated tool to establish tight bounds on the amortized space-time cost of computing the function. We show that such a tool is unlikely to exist in the most general case. In particular, we prove the following results.
Smoothed Analysis in Unsupervised Learning via Decoupling

Smoothed analysis is a powerful paradigm in overcoming worst-case intractability in unsupervised learning and high-dimensional data analysis. While polynomial time smoothed analysis guarantees have been obtained for worst-case intractable problems like tensor decompositions and learning mixtures of Gaussians, such guarantees have been hard to obtain for several other important problems in unsupervised learning. A core technical challenge in analyzing algorithms is obtaining lower bounds on the least singular value for random matrix ensembles with dependent entries, that are given by low-degree polynomials of a few base underlying random variables. In this work, we address this challenge by obtaining high-confidence lower bounds on the least singular value of new classes of structured random matrix ensembles of the above kind. We then use these bounds to design algorithms with polynomial time smoothed analysis guarantees for the following three important problems in unsupervised learning: •Robust subspace recovery, when the fraction of inliers in the d-dimensional subspace T of the n-dimensional Euclidean space is at least (d/n)^t for any positive integer t. This contrasts with the known worst-case intractability when the fraction of inliers is at most d/n, and the previous smoothed analysis result (Hardt and Moitra, 2013). •Learning overcomplete hidden markov models, where the size of the state space is any polynomial in the dimension of the observations. This gives the first polynomial time guarantees for learning overcomplete HMMs in the smoothed analysis model. •Higher order tensor decompositions, where we generalize and analyze the so-called FOOBI algorithm of Cardoso to find order-t rank-one tensors in a subspace. This gives polynomially robust decomposition algorithms for order-2t tensors with rank n^t.
Complications in Children with Ehlers-Danlos Syndrome Following Spine Surgery: Analysis of the Pediatric National Surgery Quality Improvement Program Database

Background Ehlers-Danlos syndrome (EDS) is a group of rare congenital disorders of connective tissue that result in tissue fragility and joint hyperextensibility. Owing to its rarity, outcomes of pediatric spine surgery in patients with EDS are poorly characterized. Although it has been suggested that complication rates are high, few studies have characterized these complications. Methods Pediatric National Surgery Quality Improvement Program data from 2012–2016 were analyzed. Patients with EDS undergoing spine surgery were identified along with patients without EDS undergoing the same surgeries using International Classification of Diseases, Ninth Revision and Current Procedural Terminology codes. Results Of 369,176 total patients, 279 were determined to have EDS. Of these, 56 patients underwent spine surgery; 46% were male and 54% were female (P = 0.108). Mean age at surgery was 11.59 years (P = 0.888) with a range of 1.77–17.33 years. The most common procedure was arthrodesis (n = 37). There were no differences in unplanned reoperations (n = 4, P = 0.119), wound infections or disruptions (n = 2, P = 0.670), or overall complications (n = 25, P = 0.751). Blood transfusions were required in 41% of patients with EDS, but this was not significant compared with patients without EDS undergoing the same procedures (n = 23, P = 0.580). The total amount of blood transfused (P = 0.508), length of hospital stay (P = 0.396), and total operative time (P = 0.357) were not different from control subjects. Conclusions Pediatric patients with EDS do not appear to be at a higher risk of bleeding or other complications during spine surgery as reported in past case series. This is the largest retrospective review of its kind that has been performed in this patient population.
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection

Joint analysis of data from multiple information repositories facilitates uncovering the underlying structure in heterogeneous datasets. Single and coupled matrix-tensor factorization (CMTF) has been widely used in this context for imputation-based recommendation from ratings, social network, and other user-item data. When this side information is in the form of item-item correlation matrices or graphs, existing CMTF algorithms may fall short. Alleviating current limitations, we introduce a novel model coined coupled graph-tensor factorization (CGTF) that judiciously accounts for graph-related side information. The CGTF model has the potential to overcome practical challenges, such as missing slabs from the tensor and/or missing rows/columns from the correlation matrices. A novel alternating direction method of multipliers (ADMM) is also developed that recovers the nonnegative factors of CGTF. Our algorithm enjoys closed-form updates that result in reduced computational complexity and allow for convergence claims. A novel direction is further explored by employing the interpretable factors to detect graph communities having the tensor as side information. The resulting community detection approach is successful even when some links in the graphs are missing. Results with real data sets corroborate the merits of the proposed methods relative to state-of-the-art competing factorization techniques in providing recommendations and detecting communities.
TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a Street View environment to a goal position, and then guess a location in its observed environment described in natural language to find a hidden object. The data contains 9326 examples of English instructions and spatial descriptions paired with demonstrations. We perform qualitative linguistic analysis, and show that the data displays a rich use of spatial reasoning. Empirical analysis shows the data presents an open challenge to existing methods.
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network Using Truncated Gaussian Approximation

In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource-limited mobile systems. As the countermeasure to this problem, deep neural networks with ternarized weights (i.e. -1, 0, +1) have been widely explored to greatly reduce model size and computational cost, with limited accuracy degradation. In this work, we propose a novel ternarized neural network training method which simultaneously optimizes both weights and quantizer during training, differentiating from prior works. Instead of fixed and uniform weight ternarization, we are the first to incorporate the thresholds of weight ternarization into a closed-form representation using truncated Gaussian approximation, enabling simultaneous optimization of weights and quantizer through back-propagation training. With both of the first and last layer ternarized, the experiments on the ImageNet classification task show that our ternarized ResNet-18/34/50 only has 3.9/2.52/2.16% accuracy degradation in comparison to the full-precision counterparts.
Analysis and Optimization of I/O Cache Coherency Strategies for SoC-FPGA Device

Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA devices provide tighter integrations between software running on CPUs and hardware accelerators. Modern heterogeneous SoC-FPGA platforms support multiple I/O cache coherence options between CPUs and FPGAs, but these options can have inadvertent effects on the achieved bandwidths depending on applications and data access patterns. To provide the most efficient communications between CPUs and accelerators, understanding the data transaction behaviors and selecting the right I/O cache coherence method is essential. In this paper, we use Xilinx Zynq UltraScale+ as the SoC platform to show how certain I/O cache coherence method can perform better or worse in different situations, ultimately affecting the overall accelerator performances as well. Based on our analysis, we further explore possible software and hardware modifications to improve the I/O performances with different I/O cache coherence options. With our proposed modifications, the overall performance of SoC design can be averagely improved by 20%.
Volume of perturbations of pseudoeffective classes

In this short note, we consider the question of determining the asymptotics of the volume function near the boundary of the pseudoeffective cone on compact Kahler manifolds. We solve the question in a number of cases -- in particular, we show that the volume function behaves polynomially under small perturbations near pseudoeffective classes with numerical dimension zero.
BOINC: A Platform for Volunteer Computing

“Volunteer computing” is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC’s features, architecture, implementation, and algorithms.
Strong laws of large numbers for arrays of random variables and stable random fields

Abstract Strong laws of large numbers are established for random fields with weak or strong dependence. These limit theorems are applicable to random fields with heavy-tailed distributions including fractional stable random fields. The conditions for SLLN are described in terms of the p-th moments of the partial sums of the random fields, which are convenient to verify. The main technical tool in this paper is a maximal inequality for the moments of partial sums of random fields that extends the technique of Levental, Chobanyan and Salehi [6] for a sequence of random variables indexed by a one-parameter.
The effects of terminal tagging on homomeric interactions of the sigma 1 receptor

The sigma 1 receptor (σ1R) has been implicated in cancers, neurological disorders, and substance use disorders. Yet, its molecular and cellular functions have not been well-understood. Recent crystal structures of σ1R reveal a single N-terminal transmembrane segment and C-terminal ligand-binding domain, and a trimeric organization. Nevertheless, outstanding issues surrounding the functional or pharmacological relevance of σ1R oligomerization remain, such as the minimal protomeric unit and the differentially altered oligomerization states by different classes of ligands. Western blot (WB) assays have been widely used to investigate protein oligomerizations. However, the unique topology of σ1R renders several intertwined challenges in WB. Here we describe a WB protocol without temperature denaturization to study the ligand binding effects on the oligomerization state of σ1R. Using this approach, we observed unexpected ladder-like incremental migration pattern of σ1R, demonstrating preserved homomeric interactions in the detergent environment. We compared the migration patterns of intact σ1R construct and the C-terminally tagged σ1R constructs, and found similar trends in response to drug treatments. In contrast, N-terminally tagged σ1R constructs show opposite trends to that of the intact construct, suggesting distorted elicitation of the ligand binding effects on oligomerization. Together, our findings indicate that the N-terminus plays an important role in eliciting the impacts of bound ligands, whereas the C-terminus is amenable for modifications for biochemical studies.
Beyond JAK-STAT: novel therapeutic targets in Ph-negative MPN

The Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) share a common pathobiology of constitutive activation of the JAK and STAT pathway, despite having the 3 distinct phenotypes of essential thrombocythemia, polycythemia vera, and primary myelofibrosis. Targeting the JAK-STAT pathway has led to remarkable clinical benefit, including reduction in splenomegaly, amelioration of cytokine-driven symptoms, improvement in quality of life, and even some improvement in survival. However, targeting this pathway has not resulted in consistent disease modification by current metrics, including a reduction in mutant allele burden or reversal of fibrosis. Moreover, targeting JAK-STAT can lead to limiting treatment-emergent side effects, such as anemia and thrombocytopenia. Continued discovery points to a complex system of pathogenesis beyond JAK-STAT driving the formation and evolution of MPNs. This article reviews the successes and limitations of JAK-STAT inhibition, surveys the strategies behind emerging therapies, and discusses the challenges that are present in moving beyond JAK-STAT.
Bahamians as International Adult Learners Determined for Academic Success: Challenges and Required Support System

The purpose of this study is to explore the factors that impact the experience of Caribbean nontraditional adult learners encounter while pursuing higher education in the United States and perceived support systems needed by Caribbean adult learners to be successful. On this premise, this qualitative study was conducted. A total of 15 Bahamian students participated. Data was collected via semi-structured interviews and analyzed utilizing open coding. The major themes that emerged with regards to influential factors that impacted their experiences were: (a) financial constraints, (b) lack of support, and (c) cultural differences and adaptation. As it relates to support systems needed by Caribbean students, the major themes that emerged were: (a) family, (b) faculty/mentors, and (c) a network of friends.
The Amyloid-Tau-Neuroinflammation Axis in the Context of Cerebral Amyloid Angiopathy.

Cerebral amyloid angiopathy (CAA) is typified by the cerebrovascular deposition of amyloid. Currently, there is no clear understanding of the mechanisms underlying the contribution of CAA to neurodegeneration. Despite the fact that CAA is highly associated with the accumulation of Aβ, other types of amyloids have been shown to associate with the vasculature. Interestingly, in many cases, vascular amyloidosis has been associated with an active immune response and perivascular deposition of hyperphosphorylated tau. Despite the fact that in Alzheimer’s disease (AD) a major focus of research has been the understanding of the connection between parenchymal amyloid plaques, tau aggregates in the form of neurofibrillary tangles (NFTs), and immune activation, the contribution of tau and neuroinflammation to neurodegeneration associated with CAA remains understudied. In this review, we discussed the existing evidence regarding the amyloid diversity in CAA and its relation to tau pathology and immune response, as well as the possible contribution of molecular and cellular mechanisms, previously associated with parenchymal amyloid in AD and AD-related dementias, to the pathogenesis of CAA. The detailed understanding of the “amyloid-tau-neuroinflammation” axis in the context of CAA could open the opportunity to develop therapeutic interventions for dementias associated with CAA that are currently being proposed for AD and AD-related dementias.
Robust latent-variable interpretation of in vivo regression models by nested resampling.

Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell–molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular–phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, where replicate-to-replicate variance is sufficiently low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step for interpreting the robustness of in vivo regression models.
Deep Learning at the Edge

The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device is an electronic device that provides connections to service providers and other edge devices; typically, such devices have limited resources. Since edge devices are resource-constrained, the task of launching algorithms, methods, and applications onto edge devices is considered to be a significant challenge. In this paper, we discuss one of the most widely used machine learning methods, namely, Deep Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge computing paradigm. We also provide relevant discussions about selected applications that would greatly benefit from DL at the edge.
Estimation of firm-level productivity in the presence of exports: Evidence from China's manufacturing

Motivated by the longstanding interest of economists in understanding the nexus between firm productivity and export behavior, this paper develops a novel structural framework for control-function-based nonparametric identification of the gross production function and latent firm productivity in the presence of endogenous export opportunities that is robust to recent unidentification critiques of proxy estimators. We provide a workable identification strategy, whereby the firm's degree of export orientation provides the needed (excluded) relevant independent exogenous variation in endogenous freely varying inputs, thus allowing us to identify the production function. We estimate our fully nonparametric IV model using the Landweber-Fridman regularization with the unknown functions approximated via artificial neural network sieves with a sigmoid activation function which are known for their superior performance relative to other popular sieve approximators, including the polynomial series favored in the literature. Using our methodology, we obtain robust productivity estimates for manufacturing firms from twenty eight industries in China during the 1999-2006 period to take a close look at China's exporter productivity puzzle, whereby exporters are found to exhibit lower productivity levels than non-exports.
Metabolic insight into bacterial community assembly across ecosystem boundaries.

The movement of organisms across habitat boundaries has important consequences for populations, communities, and ecosystems. However, because most species are not well adapted to all habitat types, dispersal into suboptimal habitats could induce physiological changes associated with persistence strategies that influence community assembly. For example, high rates of cross-boundary dispersal are thought to maintain sink populations of terrestrial bacteria in aquatic habitats, but these bacteria may also persist by lowering their metabolic activity, introducing metabolic heterogeneity that buffers the population against species sorting. To differentiate between these assembly processes, we analyzed bacterial composition along a hydrological flow path from terrestrial soils through an aquatic reservoir by sequencing the active and total (active + inactive) portions of the community. When metabolic heterogeneity was ignored, our data were consistent with views that cross-boundary dispersal is important for structuring aquatic bacterial communities. In contrast, we found evidence for strong species sorting in the active portion of the aquatic community, suggesting that dispersal may have a weaker effect than persistence strategies on aquatic community assembly. By accounting for metabolic heterogeneity in complex communities, our findings clarify the roles of local- and regional-scale assembly processes in terrestrial-aquatic meta-ecosystems.