cssi/SciSciGPT-SciSciNet huggingface dataset:
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates

We establish the first nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators where feature vectors reside in metric spaces. Our bounds imply rates of strong consistency for these nonparametric estimators and, up to a log factor, match an existing lower bound for conditional CDF estimation. Our proof strategy also yields nonasymptotic guarantees for nearest neighbor and kernel variants of the Nelson-Aalen cumulative hazards estimator. We experimentally compare these methods on four datasets. We find that for the kernel survival estimator, a good choice of kernel is one learned using random survival forests.
TiCl4 Promoted Three Component Coupling Reaction : A New Method for the Synthesis of Functionalized Tetrahydrofurans and Tetrahydropyrans.

Abstract TiCl4 promoted coupling of ethyl glyoxylate and dihydrofuran or dihydropyran provided oxonium ion intermediates which upon reaction with a nucleophilic trapping agent such as allyltrimethylsilane provided 2,3-disubstituted tetrahydrofurans and pyrans in good yields. The overall protocol constitutes an efficient three component coupling reaction in one pot.
Rearrangements of pyrrole and indole substituted enol esters of cyclohexane-1,3-dione

Enol esters 3a and 3b, from cyclohexane-1,3-dione and pyrrole-2-carbonyl chloride and indole-2-carbonyl chloride, respectively, rearranged in the presence of triethylamine to the enamino acids 7 and 12. In the presence of cyanide, 3b, also underwent the expected rearrangement to 4b, whereas only 7 was formed from 3a. Treatment of 7 with mercuric acetate in hot acetic acid resulted in decarboxylation and aromatization to phenol 9.
Spin dimer and electronic band structure analyses of the ferromagnetism versus antiferromagnetism in SeCuO3 and TeCuO3

On the basis of spin dimer analysis and density functional theory electronic band structure calculations, we examined why the magnetic ground state of SeCuO3 is ferromagnetic while that of its isostructural analogue TeCuO3 is antiferromagnetic and estimated their spin exchange parameters. The essential difference between the magnetic properties of these oxides arises from their Cu−O(1)−Cu superexchange, but not their Cu−O(2)−Cu superexchange. Spin exchange paths relevant for understanding magnetic properties are those that contain magnetic orbitals.
A wireless centrifuge force microscope (CFM) enables multiplexed single-molecule experiments in a commercial centrifuge

The centrifuge force microscope (CFM) was recently introduced as a platform for massively parallel single-molecule manipulation and analysis. Here we developed a low-cost and self-contained CFM module that works directly within a commercial centrifuge, greatly improving accessibility and ease of use. Our instrument incorporates research grade video microscopy, a power source, a computer, and wireless transmission capability to simultaneously monitor many individually tethered microspheres. We validated the instrument by performing single-molecule force shearing of short DNA duplexes. For a 7 bp duplex, we observed over 1000 dissociation events due to force dependent shearing from 2 pN to 12 pN with dissociation times in the range of 10-100 s. We extended the measurement to a 10 bp duplex, applying a 12 pN force clamp and directly observing single-molecule dissociation over an 85 min experiment. Our new CFM module facilitates simple and inexpensive experiments that dramatically improve access to single-molecule analysis.
Threshold regulation and stochasticity from the MecA/ClpCP proteolytic system in Streptococcus mutans competence

Many bacterial species use the MecA/ClpCP proteolytic system to block entry into genetic competence. In Streptococcus mutans, MecA/ClpCP degrades ComX (also called SigX), an alternative sigma factor for the comY operon and other late competence genes. Although the mechanism of MecA/ClpCP has been studied in multiple Streptococcus species, its role within noisy competence pathways is poorly understood. S. mutans competence can be triggered by two different peptides, CSP and XIP, but it is not known whether MecA/ClpCP acts similarly for both stimuli, how it affects competence heterogeneity, and how its regulation is overcome. We have studied the effect of MecA/ClpCP on the activation of comY in individual S. mutans cells. Our data show that MecA/ClpCP is active under both XIP and CSP stimulation, that it provides threshold control of comY, and that it adds noise in comY expression. Our data agree quantitatively with a model in which MecA/ClpCP prevents adventitious entry into competence by sequestering or intercepting low levels of ComX. Competence is permitted when ComX levels exceed a threshold, but cell-to-cell heterogeneity in MecA levels creates variability in that threshold. Therefore, MecA/ClpCP provides a stochastic switch, located downstream of the already noisy comX, that enhances phenotypic diversity.
Positional chemoselectivity in the Zn(II)-mediated removal of phenol protecting groups

Abstract A protocol was developed for the chemoselective ortho- deprotection of polyphenolic substrates using readily available Zn II X 2 salts. This procedure provides exceptional positional selectivity for the deprotection of phenols that reside adjacent to directing carbonyl functionality in the presence of similar protecting groups at the meta and para positions. Good to excellent yields of the desired free phenols were obtained (⩽96%), and a wide assortment of protecting groups was readily removed under the reaction conditions.
The evolutionary trade‐off between stem cell niche size, aging, and tumorigenesis

Many epithelial tissues within multicellular organisms are continually replenished by small independent populations of stem cells largely responsible for maintaining tissue homeostasis. These continually dividing populations are subject to mutations that can lead to tumorigenesis but also contribute to aging. Mutations accumulate in stem cell niches and change the rate of cell division and differentiation; the pace of this process and the fate of specific mutations depend strongly on niche population size. Here, we create a mathematical model of the intestinal stem cell niche, crypt system, and epithelium. We calculate the expected effect of fixed mutations in stem cell niches and their effect on tissue homeostasis throughout the intestinal epithelium over organismal lifetime. We find that, due to the small population size of stem cell niches, mutations predominantly fix via genetic drift and decrease stem cell fitness, leading to niche and tissue attrition, and contributing to organismal aging. We also explore mutation accumulation at various stem cell niche sizes and demonstrate that an evolutionary trade-off exists between niche size, tissue aging, and the risk of tumorigenesis. Further, mouse and human niches exist at a size that minimizes the probability of tumorigenesis, at the expense of accumulating deleterious mutations due to genetic drift. Finally, we show that the trade-off between the probability of tumorigenesis and the extent of aging depends on whether or not mutational effects confer a selective advantage in the stem cell niche.
Enantioselective Synthesis of 3-Deoxy-(R)-sphingomyelin from (S)-1-(4'-Methoxyphenyl)glycerol.

(R)-3-Deoxysphingomyelin (2) was prepared from (S)-1-(4‘-methoxyphenyl)-glycerol (3). The latter was converted into either p-methoxyphenyl (PMP) (S)-oxiranylmethyl ether (5) or (R)-1-(4‘-methoxyphenyl)glycerol 2,3-cyclic sulfate (6). Opening of 5 with lithium pentadecyne in the presence of BF3·Et2O gave PMP (S)-2-hydroxy-4-octadecynyl ether (7) in 65% yield. Alternatively, opening of cyclic sulfate 6 with excess lithium pentadecyne in the presence of catalytic cuprous iodide, followed by acidic workup, gave 7 in 90% yield. After introduction of the amide group via azide displacement, reduction, and N-acylation, simultaneous reduction of the triple bond and deprotection of the PMP group by Birch reduction (Li, EtNH2) provided 3-deoxy-N-palmitoyl-(R)-ceramide (9). Finally, phosphitylation of 9, oxidation of the cyclic phosphite with bromine, followed by in situ ring opening gave a (2-bromoethyl)phosphate ester, which on quaternization with aqueous trimethylamine afforded 3-deoxy-N-palmitoyl-(R)-sphingomyeli...
Predictive Value of Midtrimester Universal Cervical Length Screening Based on Parity.

OBJECTIVES To evaluate the effect of parity on performance characteristics of midtrimester cervical length (CL) in predicting spontaneous preterm birth (sPTB) before 37 weeks. METHODS This was a retrospective cohort study of 13,508 women with no history of sPTB undergoing universal transvaginal CL screening at 17 to 23 weeks' gestation from 2011 to 2016. Patients who declined screening or with unknown delivery outcomes were excluded. Areas under the receiver operator characteristic curves were used to assess and compare the predictive ability of CL screening for sPTB. The sensitivity, specificity, and positive and negative predictive values were estimated for specific CL cutoffs for prediction of sPTB. RESULTS There were 20,100 patients, of whom 2087 (10%) declined screening and 4505 (22%) did not meet inclusion criteria. Of the remaining 13,508 patients, 43% were nulliparous. The incidence rates of sPTB were 6.5% in nulliparas and 4.9% in multiparas (P < .001). The mean CLs were 39.9 mm in nulliparas and 41.8 mm in multiparas (P < .001), and those of the first percentiles were 19.0 mm in nulliparas and 24.0 mm in multiparas. Cervical length was significantly more predictive of sPTB in nulliparas (area under the curve, 0.67; 95% confidence interval, 0.63-0.70; versus 0.61, 95% confidence interval, 0.57-0.63; P = .008). At CL cutoffs of 10, 15, 20, and 25 mm or less, the sensitivity was lower in multiparas, and the specificity was comparable between the groups. CONCLUSIONS Midtrimester CL is less predictive of sPTB in multiparas compared to nulliparas. The poor predictive ability, especially in multiparas, calls into question the value of universal CL screening in this population.
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems

Data-driven control strategies for dynamical systems with unknown parameters are popular in theory and applications. An essential problem is to prevent stochastic linear systems becoming destabilized, due to the uncertainty of the decision-maker about the dynamical parameter. Two randomized algorithms are proposed for this problem, but the performance is not sufficiently investigated. Further, the effect of key parameters of the algorithms such as the magnitude and the frequency of applying the randomizations is not currently available. This work studies the stabilization speed and the failure probability of data-driven procedures. We provide numerical analyses for the performance of two methods: stochastic feedback, and stochastic parameter. The presented results imply that as long as the number of statistically independent randomizations is not too small, fast stabilization is guaranteed.
The Nollywood Nudge : An Entertaining Approach to Saving

This paper investigates the immediate and medium-term behavioral response to an emotional trigger designed to affect biases in intertemporal financial decisions. The emotional trigger is provided by a narrative portraying the catastrophic consequences of poor financial choices. Even when people are fully aware of the most appropriate action to take, cognitive biases may prevent this knowledge from translating into action. The paper contributes to the literature by directly testing the importance of linking emotional stimulus to financial messages, to influence borrowing and saving decisions, and identifying the interaction between emotional stimulus and the opportunity to act on this stimulus. The study randomly assigned individuals to a featured production -- a Nollywood (the Nigerian Hollywood) movie -- on the financial consequences of poor borrowing and saving behavior. This treatment is interacted with the option of opening a savings account at the screening of the movie. At the exit of the screening, individuals in the financial education movie treatment are more likely to open a savings account than individuals in the placebo movie treatment. However, the effects dissipate quickly. When savings and borrowing behavior is measured four months later, the study finds no differences between treatments. The paper concludes that emotional triggers delivered in the context of a one-time feature film might not be enough to secure sustained changes in behavior.
Few-shot learning-based human activity recognition

Abstract Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, a few-shot learning method for wearable sensor based human activity recognition, which is a technique that seeks high-level human activity knowledge from low-level sensor inputs, is proposed. Due to the high costs to obtain human generated activity data and the ubiquitous similarities between activity modes, it can be more efficient to borrow information from existing activity recognition models than to collect more data to train a new model from scratch when only a few data are available for model training. The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer. In order to alleviate negative transfer, a metric is proposed to measure cross-domain class-wise relevance so that knowledge of higher relevance is assigned larger weights during knowledge transfer. Promising results in extensive experiments show the advantages of the proposed approach.
Inhibiting fibroblast aggregation in skin wounds unlocks developmental pathway to regeneration

Salamanders are capable of full-thickness skin regeneration where removal of epidermis, dermis and hypodermis results in scar-free repair. What remains unclear is whether regeneration of these tissues recapitulates the cellular events of skin development or occurs through a process unique to regenerative healing. Unfortunately, information on the post-embryonic development of salamander skin is severely lacking, having focused on compartments or cell types, but never on the skin as a complete organ. By examining coordinated development of the epidermis and dermis in axolotls we establish six distinct stages of skin development (I-VI): I-V for normally paedomorphic adults and a sixth stage following metamorphosis. Raising animals either in isolation (zero density pressure) or in groups (density pressure) we find that skin development progresses as a function of animal size and that density directly effects developmental rate. Using keratins, p63, and proliferative markers, we show that when the dermis transforms into the stratum spongiosum and stratum compactum, keratinocytes differentiate into at least three distinct phenotypes that reveal a cryptic stratification program uncoupled from metamorphosis. Lastly, comparing skin regeneration to skin development, we find that dermal regeneration occurs through a unique process, relying heavily on remodeling of the wound extracellular matrix, rather than proceeding through direct development of a dermal lamella produced by the epidermis. By preventing fibroblast influx into the wound bed using beryllium nitrate, we show that in the absence of fibroblast generated ECM production skin regeneration occurs through an alternate route that recapitulates development.
Genome-wide mapping and profiling of γH2AX binding hotspots in response to different replication stress inducers

Replication stress (RS) gives rise to DNA damage that threatens genome stability. RS can originate from different sources that stall replication by diverse mechanisms. However, the mechanism underlying how different types of RS contribute to genome instability is unclear, in part due to the poor understanding of the distribution and characteristics of damage sites induced by different RS mechanisms. We use ChIP-seq to map γH2AX binding sites genome-wide caused by aphidicolin (APH), hydroxyurea (HU), and methyl methanesulfonate (MMS) treatments in human lymphocyte cells. Mapping of γH2AX ChIP-seq reveals that APH, HU, and MMS treatments induce non-random γH2AX chromatin binding at discrete regions, suggesting that there are γH2AX binding hotspots in the genome. Characterization of the distribution and sequence/epigenetic features of γH2AX binding sites reveals that the three treatments induce γH2AX binding at largely non-overlapping regions, suggesting that RS may cause damage at specific genomic loci in a manner dependent on the fork stalling mechanism. Nonetheless, γH2AX binding sites induced by the three treatments share common features including compact chromatin, coinciding with larger-than-average genes, and depletion of CpG islands and transcription start sites. Moreover, we observe significant enrichment of SINEs in γH2AX sites in all treatments, indicating that SINEs may be a common barrier for replication polymerases. Our results identify the location and common features of genome instability hotspots induced by different types of RS, and help in deciphering the mechanisms underlying RS-induced genetic diseases and carcinogenesis.
Adequate subgroups II

The notion of adequate subgroups was introduced by Thorne (J Inst Math Jussieu. arXiv:1107.5989, to appear). It is a weakening of the notion of big subgroup used by Wiles and Taylor in proving automorphy lifting theorems for certain Galois representations. Using this idea, Thorne was able to prove some new lifting theorems. It was shown in Guralnick et al. (J Inst Math Jussieu. arXiv:1107.5993, to appear) that certain groups were adequate. One of the key aspects was the question of whether the span of the semisimple elements in the group is the full endomorphism ring of an absolutely irreducible module. We show that this is the case in prime characteristic p for p-solvable groups as long the dimension is not divisible by p. We also observe that the condition holds for certain infinite groups. Finally, we present the first examples showing that this condition need not hold and give a negative answer to a question of Richard Taylor.
A unified framework for planning in adversarial and cooperative environments

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for producing legible plans in cooperative settings such that the observation sequence projected by the plan is consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or convey the actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our approach. We also present an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.
Spectral triples for the variants of the Sierpiński gasket

Fractal geometry is the study of sets which exhibit the same pattern at multiple scales. Developing tools to study these sets is of great interest. One step towards developing some of these tools is recognizing the duality between topological spaces and commutative $C^\ast$-algebras. When one lifts the commutativity axiom, one gets what are called noncommutative spaces and the study of noncommutative geometry. The tools built to study noncommutative spaces can in fact be used to study fractal sets. In what follows we will use the spectral triples of noncommutative geometry to describe various notions from fractal geometry. We focus on the fractal sets known as the harmonic Sierpinski gasket and the stretched Sierpinski gasket, and show that the spectral triples constructed by Christensen, Ivan, and Lapidus in 2008 and Lapidus and Sarhad in 2015, can recover the standard self-affine measure in the case of the harmonic Sierpinski gasket and the Hausdorff dimension, geodesic metric, and Hausdorff measure in the case of the stretched Sierpinski gasket.
Cyclic Critical Groups of Graphs

In this note, we describe a construction that leads to families of graphs whose critical groups are cyclic. For some of these families we are able to give a formula for the number of spanning trees of the graph, which then determines the group exactly.
A convex program for bilinear inversion of sparse vectors

We consider the bilinear inverse problem of recovering two vectors, x in R^L and w in R^L, from their entrywise product. We consider the case where x and w have known signs and are sparse with respect to known dictionaries of size K and N, respectively. Here, K and N may be larger than, smaller than, or equal to L. We introduce L1-BranchHull, which is a convex program posed in the natural parameter space and does not require an approximate solution or initialization in order to be stated or solved. We study the case where x and w are S1- and S2-sparse with respect to a random dictionary, with the sparse vectors satisfying an effective sparsity condition, and present a recovery guarantee that depends on the number of measurements as L > Omega(S1+S2)(log(K+N))^2. Numerical experiments verify that the scaling constant in the theorem is not too large. One application of this problem is the sweep distortion removal task in dielectric imaging, where one of the signals is a nonnegative reflectivity, and the other signal lives in a known subspace, for example that given by dominant wavelet coefficients. We also introduce a variants of L1-BranchHull for the purposes of tolerating noise and outliers, and for the purpose of recovering piecewise constant signals. We provide an ADMM implementation of these variants and show they can extract piecewise constant behavior from real images.
The fourier transform of poisson multinomial distributions and its algorithmic applications

An (n, k)-Poisson Multinomial Distribution (PMD) is a random variable of the form X = ∑i=1n Xi, where the Xi’s are independent random vectors supported on the set of standard basis vectors in k. In this paper, we obtain a refined structural understanding of PMDs by analyzing their Fourier transform. As our core structural result, we prove that the Fourier transform of PMDs is approximately sparse, i.e., its L1-norm is small outside a small set. By building on this result, we obtain the following applications: Learning Theory. We give the first computationally efficient learning algorithm for PMDs under the total variation distance. Our algorithm learns an (n, k)-PMD within variation distance e using a near-optimal sample size of Ok(1/e2), and runs in time Ok(1/e2) · logn. Previously, no algorithm with a (1/e) runtime was known, even for k=3. Game Theory. We give the first efficient polynomial-time approximation scheme (EPTAS) for computing Nash equilibria in anonymous games. For normalized anonymous games with n players and k strategies, our algorithm computes a well-supported e-Nash equilibrium in time nO(k3) · (k/e)O(k3log(k/e)/loglog(k/e))k−1. The best previous algorithm for this problem had running time n(f(k)/e)k, where f(k) = Ω(kk2), for any k>2. Statistics. We prove a multivariate central limit theorem (CLT) that relates an arbitrary PMD to a discretized multivariate Gaussian with the same mean and covariance, in total variation distance. Our new CLT strengthens the CLT of Valiant and Valiant by removing the dependence on n in the error bound. Along the way we prove several new structural results of independent interest about PMDs. These include: (i) a robust moment-matching lemma, roughly stating that two PMDs that approximately agree on their low-degree parameter moments are close in variation distance; (ii) near-optimal size proper e-covers for PMDs in total variation distance (constructive upper bound and nearly-matching lower bound). In addition to Fourier analysis, we employ a number of analytic tools, including the saddlepoint method from complex analysis, that may find other applications.
High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors.

In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding. If the number of the potential confounders (p) is larger than the number of observations (n), then direct control for all potential confounders is infeasible. Existing approaches for dimension reduction and penalization are generally aimed at predicting the outcome, and are less suited for estimation of causal effects. Under standard penalization approaches (e.g. Lasso), if a variable Xj is strongly associated with the treatment T but weakly with the outcome Y, the coefficient βj will be shrunk towards zero thus leading to confounding bias. Under the assumption of a linear model for the outcome and sparsity, we propose continuous spike and slab priors on the regression coefficients βj corresponding to the potential confounders Xj . Specifically, we introduce a prior distribution that does not heavily shrink to zero the coefficients (βj s) of the Xj s that are strongly associated with T but weakly associated with Y. We compare our proposed approach to several state of the art methods proposed in the literature. Our proposed approach has the following features: 1) it reduces confounding bias in high dimensional settings; 2) it shrinks towards zero coefficients of instrumental variables; and 3) it achieves good coverages even in small sample sizes. We apply our approach to the National Health and Nutrition Examination Survey (NHANES) data to estimate the causal effects of persistent pesticide exposure on triglyceride levels.
SparkNet: Training Deep Networks in Spark

Abstract: Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset.
The Goto numbers of parameter ideals

Abstract Let Q be a parameter ideal of a Noetherian local ring ( R , m ) . The Goto number g ( Q ) of Q is the largest integer g such that Q : m g is integral over Q . We examine the values of g ( Q ) as Q varies over the parameter ideals of R . We concentrate mainly on the case where dim R = 1 , and many of our results concern parameter ideals of a numerical semigroup ring.
An uncertainty management calculus for ordering searches in distributed dynamic databases

MINDS is a distributed system of cooperating query engines that customizes document retrieval for each user in a dynamic environment. It improves its performance and adapts to changing patterns of document distribution by observing system-user interactions and modifying the appropriate certainty factors, which act as search control parameters. It is argued here that the uncertainty management calculus mud account for temporal precedence, reliability of evidence, degree of support for a proposition, and saturation effects. The calculus presented here possesses these features. Some results obtained with this scheme are discussed.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.
Social Collaborative Retrieval

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval-a combination of these two traditional problems-has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.
Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models.

We consider multi-response and multi-task regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along with parameter inference makes sense in several applications, such as multi-response Genome-Wide Association Studies (GWAS). By inferring this additional structure we can obtain valuable information on the underlying data mechanisms (e.g. relationships among genotypes and phenotypes in GWAS). In this paper, we propose two formulations to simultaneously learn the parameter matrix and its group structures, based on convex regularization penalties. We present optimization approaches to solve the resulting problems and provide numerical convergence guarantees. Extensive experiments demonstrate much better clustering quality compared to other methods, and our approaches are also validated on real datasets concerning phenotypes and genotypes of plant varieties.
Edge Sample and Discard: A New Algorithm for Counting Triangles in Large Dynamic Graphs

Monitoring key properties of dynamic graphs in real time can help detect spam accounts in social networks, identify anomalies and intrusions, and inform resource management. Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes associated with the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model and offers significantly improved trade-off between accuracy and computational/memory costs in the running estimates of a graph’s properties. To demonstrate this higher accuracy using the framework, we present a new sampling algorithm, called Edge Sample and Discard, which generates an unbiased estimate of the total number of triangles in a fully dynamic graph. The experimental results show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better than the accuracy achieved by current state-of-the-art algorithms.
Graphons, mergeons, and so on!

In this work we develop a theory of hierarchical clustering for graphs. Our modelling assumption is that graphs are sampled from a graphon, which is a powerful and general model for generating graphs and analyzing large networks. Graphons are a far richer class of graph models than stochastic blockmodels, the primary setting for recent progress in the statistical theory of graph clustering. We define what it means for an algorithm to produce the ``correct" clustering, give sufficient conditions in which a method is statistically consistent, and provide an explicit algorithm satisfying these properties.
First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate

We study streaming principal component analysis (PCA), that is to find, in O(dk) space, the top k eigenvectors of a d&#x00D7; d hidden matrix \bold \Sigma with online vectors drawn from covariance matrix \bold \Sigma.We provide global convergence for Ojas algorithm which is popularly used in practice but lacks theoretical understanding for k&#x2248;1. We also provide a modified variant \mathsf{Oja}^{++} that runs even faster than Ojas. Our results match the information theoretic lower bound in terms of dependency on error, on eigengap, on rank k, and on dimension d, up to poly-log factors. In addition, our convergence rate can be made gap-free, that is proportional to the approximation error and independent of the eigengap.In contrast, for general rank k, before our work (1) it was open to design any algorithm with efficient global convergence rate; and (2) it was open to design any algorithm with (even local) gap-free convergence rate in O(dk) space.
On the consistency of the likelihood maximization vertex nomination scheme: bridging the gap between maximum likelihood estimation and graph matching

Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove that maximum likelihood (ML)-based vertex nomination is consistent, in the sense that the performance of the ML-based scheme asymptotically matches that of the Bayes optimal scheme. We prove theorems of this form both when model parameters are known and unknown. Additionally, we introduce and prove consistency of a related, more scalable restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex and edge features into ML-based vertex nomination and briey explore the empirical effectiveness of this approach.
Quantitative uniqueness of solutions to parabolic equations

Abstract We investigate the quantitative uniqueness of solutions to parabolic equations with lower order terms on compact smooth Riemannian manifolds. Quantitative uniqueness is a quantitative form of strong unique continuation property. We characterize quantitative uniqueness by the rate of vanishing. We can obtain the sharp vanishing order for solutions in term of the C 1 , 1 norm of the potential functions, as well as the L ∞ norm of the coefficient functions. Some new quantitative Carleman estimates and three cylinder inequalities are established.
Network Resilience Assessment via QoS Degradation Metrics: An Algorithmic Approach

This paper focuses on network resilience to perturbation of edge weight. Other than connectivity, many network applications nowadays rely upon some measure of network distance between a pair of connected nodes. In these systems, a metric related to network functionality is associated to each edge. A pair of nodes only being functional if the weighted, shortest-path distance between the pair is below a given threshold T. Consequently, a natural question is on which degree the change of edge weights can damage the network functionality? With this motivation, we study a new problem, Quality of Service Degradation : given a set of pairs, find a minimum budget to increase the edge weights which ensures the distance between each pair exceeds T . We introduce four algorithms with theoretical performance guarantees for this problem. Each of them has its own strength in trade-off between effectiveness and running time, which are illustrated both in theory and comprehensive experimental evaluation.
Isometric sketching of any set via the Restricted Isometry Property

In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices. This class includes several matrices for which matrix-vector multiply can be computed in log-linear time, providing ecient dimensionality reduction of general sets. In particular, we show that using such matrices any set from high dimensions can be embedded into lower dimensions with near optimal distortion. We obtain our results by connecting dimensionality reduction of any set to dimensionality reduction of sparse vectors via a chaining argument.
Diagonal asymptotics for symmetric rational functions via ACSV

We consider asymptotics of power series coefficients of rational functions of the form 1/Q where Q is a symmetric multilinear polynomial. We review a number of such cases from the literature, chiefly concerned either with positivity of coefficients or diagonal asymptotics. We then analyze coefficient asymptotics using ACSV (Analytic Combinatorics in Several Variables) methods. While ACSV sometimes requires considerable overhead and geometric computation, in the case of symmetric multilinear rational functions there are some reductions that streamline the analysis. Our results include diagonal asymptotics across entire classes of functions, for example the general 3-variable case and the Gillis-Reznick-Zeilberger (GRZ) case, where the denominator in terms of elementary symmetric functions is 1 - e_1 + c e_d in any number d of variables. The ACSV analysis also explains a discontinuous drop in exponential growth rate for the GRZ class at the parameter value c = (d-1)^{d-1}, previously observed for d=4 only by separately computing diagonal recurrences for critical and noncritical values of c.
Paper Abstract Writing through Editing Mechanism

We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30% and by non-expert at a rate up to 80%.
Revisiting semi-supervised learning with graph embeddings

We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.
Unsupervised cross-modal alignment of speech and text embedding spaces

Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision. Inspired by the success in unsupervised cross-lingual word embeddings, in this paper we target learning a cross-modal alignment between the embedding spaces of speech and text learned from corpora of their respective modalities in an unsupervised fashion. The proposed framework learns the individual speech and text embedding spaces, and attempts to align the two spaces via adversarial training, followed by a refinement procedure. We show how our framework could be used to perform spoken word classification and translation, and the experimental results on these two tasks demonstrate that the performance of our unsupervised alignment approach is comparable to its supervised counterpart. Our framework is especially useful for developing automatic speech recognition (ASR) and speech-to-text translation systems for low- or zero-resource languages, which have little parallel audio-text data for training modern supervised ASR and speech-to-text translation models, but account for the majority of the languages spoken across the world.
A new class of metrics for learning on real-valued and structured data.

We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result interpretation. These new distance functions unify and generalize some of the popular metrics, such as the Jaccard and bag distances on sets, Manhattan distance on vector spaces, and Marczewski-Steinhaus distance on integrable functions. We prove that the new metrics are complete and show useful relationships with f-divergences for probability distributions. To further extend our approach to structured objects such as ontologies, we introduce information-theoretic metrics on directed acyclic graphs drawn according to a fixed probability distribution. We conduct empirical investigation to demonstrate the effectiveness on real-valued, high-dimensional, and structured data. Overall, the new metrics compare favorably to multiple similarity and dissimilarity functions traditionally used in data mining, including the Minkowski ( $$L^p$$ ) family, the fractional $$L^p$$ family, two f-divergences, cosine distance, and two correlation coefficients. We provide evidence that they are particularly appropriate for rapid processing of high-dimensional and structured data in distance-based learning.
On the Controller-Stopper Problems with Controlled Jumps

We analyze the continuous time zero-sum and cooperative controller-stopper games of Karatzas and Sudderth (Ann Probab 29(3):1111–1127, 2001), Karatzas and Zamfirescu (Ann Probab 36(4):1495–1527, 2008) and Karatzas and Zamfirescu (Appl Math Optim 53(2):163–184, 2006) when the volatility of the state process is controlled as in Bayraktar and Huang (SIAM J Control Optim 51(2):1263–1297, 2013) but additionally when the state process has controlled jumps. We perform this analysis by first resolving the stochastic target problems [of Soner and Touzi (SIAM J Control Optim 41(2):404–424, 2002; J Eur Math Soc 4(3):201–236, 2002)] with a cooperative or a non-cooperative stopper and then embedding the original problem into the latter set-up. Unlike in Bayraktar and Huang (SIAM J Control Optim 51(2):1263–1297, 2013) our analysis relies crucially on the Stochastic Perron method of Bayraktar and Sirbu (SIAM J Control Optim 51(6):4274–4294, 2013) but not the dynamic programming principle, which is difficult to prove directly for games.
Quotient manifolds of flows

This paper investigates which smooth manifolds arise as quotients (orbit spaces) of flows of vector fields. Such quotient maps were already known to be surjective on fundamental groups, but this paper shows that every epimorphism of countably presented groups is induced by the quotient map of some flow, and that higher homology can also be controlled. Manifolds of fixed dimension arising as quotients of flows on Euclidean space realize all even (and some odd) intersection pairings, and all homotopy spheres of dimension at least two arise in this manner. Most Euclidean spaces of dimensions five and higher have families of topologically equivalent but smoothly inequivalent flows with quotient homeomorphic to a manifold with flexibly chosen homology. For m ≥ 2r > 2, there is a topological flow on (ℝ2r+1 − 8 points) × ℝm that is unsmoothable, although smoothable near each orbit, with quotient an unsmoothable topological manifold.
Seeded graph matching: Efficient algorithms and theoretical guarantees

In this paper, a new information theoretic framework for graph matching is introduced. Using this framework, the graph isomorphism and seeded graph matching problems are studied. The maximum degree algorithm for graph isomorphism is analyzed and sufficient conditions for successful matching are rederived using type analysis. Furthermore, a new seeded matching algorithm with polynomial time complexity is introduced. The algorithm uses ‘typicality matching’ and techniques from point-to-point communications for reliable matching. Assuming an Erdos-Renyi model on the correlated graph pair, it is shown that successful matching is guaranteed when the number of seeds grows logarithmically with the number of vertices in the graphs. The logarithmic coefficient is shown to be inversely proportional to the mutual information between the edge variables in the two graphs.
Cell-probe lower bounds from online communication complexity

In this work, we introduce an online model for communication complexity. Analogous to how online algorithms receive their input piece-by-piece, our model presents one of the players, Bob, his input piece-by-piece, and has the players Alice and Bob cooperate to compute a result each time before the next piece is revealed to Bob. This model has a closer and more natural correspondence to dynamic data structures than classic communication models do, and hence presents a new perspective on data structures. We first present a tight lower bound for the online set intersection problem in the online communication model, demonstrating a general approach for proving online communication lower bounds. The online communication model prevents a batching trick that classic communication complexity allows, and yields a stronger lower bound. We then apply the online communication model to prove data structure lower bounds for two dynamic data structure problems: the Group Range problem and the Dynamic Connectivity problem for forests. Both of the problems admit a worst case O(logn)-time data structure. Using online communication complexity, we prove a tight cell-probe lower bound for each: spending o(logn) (even amortized) time per operation results in at best an exp(−δ2 n) probability of correctly answering a (1/2+δ)-fraction of the n queries.