cssi/SciSciGPT-SciSciNet huggingface dataset:
Towards predicting the impact of software changes on building activities

The pervasive adoption of Continuous Integration practices – both in industry and open source projects – has led software building to become a daily activity for thousands of developers around the world. Companies such as Microsoft have invested in in-house infrastructures with the goal of optimizing the build process. CloudBuild, a distributed and caching build service developed internally by Microsoft, runs the build process in parallel in the cloud and relies on caching to accelerate builds. This allows for agile development and rapid delivery of software even several times a day. However, moving towards faster builds requires not only improvements on the infrastructure side, but also attention to developers' changes in the software. Surely, architectural decisions and software changes, such as addition of dependencies, can lead to significant build time increase. Yet, estimating the impact of such changes on build time can be challenging when dealing with complex, distributed, and cached build systems. In this paper, we envision a predictive model able to preemptively alert developers on the extent to which their software changes may impact future building activities. In particular, we describe an approach that analyzes the developer's change and predicts (i) whether it impacts (any of) the Longest Critical Path; (ii) may lead to build time increase and its delta; and (iii) the percentage of future builds that might be affected by such change.
Doctors and diversity: Using interfaith literacy and interfaith dialogue to improve patient care

In an increasingly diversifying world, how can doctors and other health care professionals improve patient care? Dr. Diana Eck, Director of the Pluralism Project at Harvard University, claims that the United States has become the most religiously diverse nation in the world. Shockingly, the medical field neglects discussion about how these changing religious demographics affect the practice of medicine. Currently, in spite of the fact that religious differences have the serious potential to create obstacles in developing the patient-doctor relationship, many doctors unfortunately do not understand how to approach religious differences, or recognize their effect on patient health. For example, what if a doctor who is ignorant of Islam’s prohibition of the consumption of pork prescribes a Muslim patient Heparin, a porcine product? Would the patient unknowingly defile themselves, become noncompliant, or even pursue litigation? This paper, written by one of Concordia College’s Interfaith Scholars, proposes that doctors must 1) acquire interfaith literacy in medical school through required courses and 2) learn the skills necessary to engage in interfaith dialogue with patients in their practice in order to provide the best patient care in areas with growing diverse patient populations. The author of this essay engaged in interfaith interviews with doctors from non-majority religious traditions such as Hinduism and Islam and researched the current status of medical education and the occurrence of religious dialogue between patients and doctors nationally. This paper uses the theoretical work of Dr. Eboo Patel, author of Sacred Ground: Pluralism, Prejudice, and the Promise of America to argue that interfaith literacy is necessary to understand different spiritual backgrounds and to build compassionate care for patients with religious differences. Interfaith dialogue is the best method for discussing a patient’s religious background because it is non-conversional at its core and allows for patients to describe their own experiences and expectations, both of which may have a substantive impact on the care that they need or are willing to accept. This paper concludes interfaith literacy and dialogue must be taught in medical schools and practiced by doctors, because institutionalizing this change will ensure improved and more holistic patient care for those of diverse religious backgrounds.
Distributed optimization of deeply nested systems

Intelligent processing of complex signals such as images is often performed by a hierarchy of nonlinear processing layers, such as a deep net or an object recognition cascade. Joint estimation of the parameters of all the layers is a difficult nonconvex optimization. We describe a general strategy to learn the parameters and, to some extent, the architecture of nested systems, which we call the method of auxiliary coordinates (MAC). This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, can perform some model selection on the fly, and is competitive with stateof-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations. The continued increase in recent years in data availability and processing power has enabled the development and practical applicability of ever more powerful models in statistical machine learning, for example to recognize faces or speech, or to translate natural language. However, physical limitations in serial computation suggest that scalable processing will require algorithms that can be massively parallelized, so they can profit from the thousands of inexpensive processors available in cloud computing. We focus on hierarchical, or nested, processing architectures. As a particular but important example, consider deep neuAppearing in Proceedings of the 17 International Conference on Artificial Intelligence and Statistics (AISTATS) 2014, Reykjavik, Iceland. JMLR: W&CP volume 33. Copyright 2014 by the authors. ral nets (fig. 1), which were originally inspired by biological systems such as the visual and auditory cortex in the mammalian brain (Serre et al., 2007), and which have been proven very successful at learning sophisticated tasks, such as recognizing faces or speech, when trained on data.
Results and open problems in matchings in regular graphs

This survey paper deals with upper and lower bounds on the number of k-matchings in regular graphs on N vertices. For the upper bounds we recall the upper matching conjecture which is known to hold for perfect matchings. For the lower bounds we first survey the known results for bipartite graphs, and their continuous versions as the van der Waerden and Tverberg permanent conjectures and its variants. We then discuss non-bipartite graphs. Little is known beyond the recent proof of the Lovasz-Plummer conjecture on the exponential growth of perfect matchings in cubic bridgeless graphs. We discuss the problem of the minimum of haffnians on the convex set of matrices, whose extreme points are the adjacency matrices of subgraphs of the complete graph corresponding to perfect matchings. We also consider infinite regular graphs. The analog of k-matching is the p-monomer entropy, where p ∈ [0, 1] is the density of the number of matchings. AMS subject classifications. 05C30, 05C70, 15A15.
Fast MCMC sampling algorithms on polytopes

We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for generating samples from the uniform distribution over a polytope. Both random walks are sampling algorithms derived from interior point methods. The former is based on volumetric-logarithmic barrier introduced by Vaidya whereas the latter uses John's ellipsoids. We show that the Vaidya walk mixes in significantly fewer steps than the logarithmic-barrier based Dikin walk studied in past work. For a polytope in ℝd defined by n ≥ d linear constraints, we show that the mixing time from a warm start is bounded as O(n0.5d1.5), compared to the O(nd) mixing time bound for the Dikin walk. The cost of each step of the Vaidya walk is of the same order as the Dikin walk, and at most twice as large in terms of constant pre-factors. For the John walk, we prove an O(d2.5 log4(n/d) bound on its mixing time and conjecture that an improved variant of it could achieve a mixing time of O(d2 poly-log(n/d)). Additionally, we propose variants of the Vaidya and John walks that mix in polynomial time from a deterministic starting point. The speed-up of the Vaidya walk over the Dikin walk are illustrated in numerical examples.
Online dual coordinate ascent learning

The stochastic dual coordinate-ascent (S-DCA) technique is a useful alternative to the traditional stochastic gradient-descent algorithm for solving large-scale optimization problems due to its scalability to large data sets and strong theoretical guarantees. However, the available S-DCA formulation is limited to finite sample sizes and relies on performing multiple passes over the same data. This formulation is not well-suited for online implementations where data keep streaming in. In this work, we develop an online dual coordinate-ascent (O-DCA) algorithm that is able to respond to streaming data and does not need to revisit the past data. This feature infuses the resulting construction with continuous adaptation, learning, and tracking abilities, which are particularly useful for online learning scenarios.
Tight Bounds on Proper Equivalence Query Learning of DNF

We prove a new structural lemma for partial Boolean functionsf , which we call the seed lemma for DNF. Using the lemma, we give the first subexponential algorithm for proper learning of poly(n)term DNF in Angluin’s Equivalence Query (EQ) model. The algorithm has time and query complexity 2 ( ~ O p n) , which is optimal. We also give a new result on certificates for DNF-size, a simple
Tensor Categories Attached to Exceptional Cells in Weyl Groups

Using truncated convolution of perverse sheaves on a flag variety Lusztig associated a monoidal category to a two sided cell in the Weyl group. We identify this category in the case which was not decided previously.
Cohomology of presheaves with oriented weak transfers

Over a field of characteristic zero, we establish the homotopy invariance of the Nisnevich cohomology of homotopy invariant presheaves with oriented weak transfers, and the agreement of Zariski and Nisnevich cohomology for such presheaves. This generalizes a foundational result in Voevodsky's theory of motives. The main idea is to find explicit smooth representatives of the correspondences which provide the input for Voevodsky's cohomological architecture.
Women’s Representation in Mathematics Subfields: Evidence from the arXiv

We use data from papers posted to the Mathematics section of the arXiv to explore the representation of women in mathematics research. We show that women are under-represented as authors of mathematics papers on the arXiv, even in comparison to the proportion of women who hold full-time positions in mathematics departments. However, some subfields have much greater participation than others.
Normal number constructions for Cantor series with slowly growing bases

Let Q = (qn)n=1∞ be a sequence of bases with qi ≥ 2. In the case when the qi are slowly growing and satisfy some additional weak conditions, we provide a construction of a number whose Q-Cantor series expansion is both Q-normal and Q-distribution normal. Moreover, this construction will result in a computable number provided we have some additional conditions on the computability of Q, and from this construction we can provide computable constructions of numbers with atypical normality properties.
Compactified moduli of projective bundles

We present a method for compactifying stacks of PGLn-torsors (Azumaya algebras) on algebraic spaces. In particular, when the ambient space is a smooth projective surface we use our methods to show that various moduli spaces are irreducible and carry natural virtual fundamental classes. We also prove a version of the Skolem–Noether theorem for certain algebra objects in the derived category, which allows us to give an explicit description of the boundary points in our compactified moduli problem.
Coevolutionary Neural Population Models

We present a method for using neural networks to model evolutionary population dynamics, and draw parallels to recent deep learning advancements in which adversarially-trained neural networks engag...
Together we know how to achieve: An epistemic logic of know-how

Abstract The existence of a coalition strategy to achieve a goal does not necessarily mean that the coalition has enough information to know how to follow the strategy. Neither does it mean that the coalition knows that such a strategy exists. The article studies an interplay between the distributed knowledge, coalition strategies, and coalition “know-how” strategies. The main technical result is a sound and complete trimodal logical system that describes the properties of this interplay.
SufiSent - Universal Sentence Representations Using Suffix Encodings.

Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a method to learn such representations by encoding the suffixes of word sequences in a sentence and training on the Stanford Natural Language Inference (SNLI) dataset. We demonstrate the effectiveness of our approach by evaluating it on the SentEval benchmark, improving on existing approaches on several transfer tasks.
Balanced Allocations: A Simple Proof for the Heavily Loaded Case

We give a new proof for the fact that the expected gap between the maximum load and the average load in the two-choice process is bounded by (1 + o(1))loglogn, irrespective of the number of balls thrown. The original proof of this surprising result, due to Berenbrink et al. in [2], uses tools from Markov chain theory, and a sophisticated induction proof involving computer-aided calculations. We provide a significantly simpler and more elementary proof. The new technique allows us to generalize the result and derive new and often tight bounds for the case of weighted balls. The simplification comes at a cost of larger lower order terms and a weaker tail bound for the probability of deviating from the expectation.
Diameter of Ramanujan Graphs and Random Cayley Graphs

We study the diameter of LPS Ramanujan graphs Xp,q. We show that the diameter of the bipartite Ramanujan graphs is greater than (4/3)logp(n)+O(1), where n is the number of vertices of Xp,q. We also construct an infinite family of (p+1)-regular LPS Ramanujan graphs Xp,m such that the diameter of these graphs is greater than or equal to ⌊(4/3)logp(n)⌋. On the other hand, for any k-regular Ramanujan graph we show that only a tiny fraction of all pairs of vertices have distance greater than (1+ϵ) logk–1(n). We also have some numerical experiments for LPS Ramanujan graphs and random Cayley graphs which suggest that the diameters are asymptotically (4/3)logk–1(n) and logk–1(n), respectively.
Dissipative dynamics of planar d-wave Josephson junctions.

We study quantum dynamics of and phase transitions in a Josephson junction between two planar $d$-wave superconductors where the processes of both quasiparticle and Cooper pair tunneling give rise to nonlocal dissipative terms in the effective action. By combining a perturbative weak coupling analysis in the charge representation with a variational approach in the phase representation at strong coupling, we ascertain a layout of the junction's phase diagram and discuss the corresponding behaviors.
Scheduling wireless ad hoc networks in polynomial time using claw-free conflict graphs

In this paper, we address the scheduling problem in wireless ad hoc networks by exploiting the computational advantage that comes when such scheduling problems can be represented by claw-free conflict graphs. It is possible to formulate a scheduling problem of network coded flows as finding maximum weighted independent set (MWIS) in the conflict graph of the network. We consider activation of hyperedges in a hypergraph to model a wireless broadcast medium. We show that the conflict graph of certain wireless ad hoc networks are claw-free. It is known that finding MWIS of a general graph is NP-hard, but in a claw-free conflict graph, it is possible to apply Minty's or Faenza et al.'s algorithms in polynomial time. We discuss our approach on some sample networks.
ON n-PUNCTURED BALL TANGLES

We consider a class of topological objects in the 3-sphere S3 which will be called n-punctured ball tangles. Using the Kauffman bracket at A = eiπ/4, an invariant for a special type of n-punctured ball tangles is defined. The invariant F takes values in PM2×2n(ℤ), that is the set of 2 × 2n matrices over ℤ modulo the scalar multiplication of ±1. This invariant leads to a generalization of a theorem of Krebes which gives a necessary condition for a given collection of tangles to be embedded in a link in S3 disjointly. We also address the question of whether the invariant F is surjective onto PM2×2n(ℤ). We will show that the invariant F is surjective when n = 0. When n = 1, n-punctured ball tangles will also be called spherical tangles We show that det F(S) ≡ 0 or 1 mod 4 for every spherical tangle S. Thus, F is not surjective when n = 1.
Yau–Tian–Donaldson correspondence for K-semistable Fano manifolds

In this paper, using the recent compactness results of Tian and Chen-Donaldson-Sun, we prove the K-semistable version of Yau-Tian-Donaldson correspondence for Fano manifolds.
Deep Learning Under Privileged Information Using Heteroscedastic Dropout

Unlike machines, humans learn through rapid, Abstract model-building. The role of a teacher is not simply to hammer home right or wrong answers, but rather to provide intuitive comments, comparisons, and explanations to a pupil. This is what the Learning Under Privileged Information (LUPI) paradigm endeavors to model by utilizing extra knowledge only available during training. We propose a new LUPI algorithm specifically designed for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We propose to use a heteroscedastic dropout (i.e. dropout with a varying variance) and make the variance of the dropout a function of privileged information. Intuitively, this corresponds to using the privileged information to control the uncertainty of the model output. We perform experiments using CNNs and RNNs for the tasks of image classification and machine translation. Our method significantly increases the sample efficiency during learning, resulting in higher accuracy with a large margin when the number of training examples is limited. We also theoretically justify the gains in sample efficiency by providing a generalization error bound decreasing with O(1/n), where n is the number of training examples, in an oracle case.
Information Complexity versus Corruption and Applications to Orthogonality and Gap-Hamming

Three decades of research in communication complexity have led to the invention of a number of techniques to lower bound randomized communication complexity. The majority of these techniques involve properties of large submatrices (rectangles) of the truth-table matrix defining a communication problem. The only technique that does not quite fit is information complexity, which has been investigated over the last decade. Here, we connect information complexity to one of the most powerful “rectangular” techniques: the recently-introduced smooth corruption (or “smooth rectangle”) bound. We show that the former subsumes the latter under rectangular input distributions.
Sequence-Dependent Three Interaction Site Model for Single- and Double-Stranded DNA

We develop a robust coarse-grained model for single- and double-stranded DNA by representing each nucleotide by three interaction sites (TIS) located at the centers of mass of sugar, phosphate, and base. The resulting TIS model includes base-stacking, hydrogen bond, and electrostatic interactions as well as bond-stretching and bond angle potentials that account for the polymeric nature of DNA. The choices of force constants for stretching and the bending potentials were guided by a Boltzmann inversion procedure using a large representative set of DNA structures extracted from the Protein Data Bank. Some of the parameters in the stacking interactions were calculated using a learning procedure, which ensured that the experimentally measured melting temperatures of dimers are faithfully reproduced. Without any further adjustments, the calculations based on the TIS model reproduce the experimentally measured salt and sequence-dependence of the size of single-stranded DNA (ssDNA), as well as the persistence le...
An Index Formula for Supersymmetric Quantum Mechanics

We derive a localization formula for the refined index of gauged quantum mechanics with four supercharges. Our answer takes the form of a residue integral on the complexified Cartan subalgebra of the gauge group. The formula captures the dependence of the index on Fayet-Iliopoulos parameters and the presence of a generic superpotential. The residue formula provides an efficient method for computing cohomology of quiver moduli spaces. Our result has broad applications to the counting of BPS states in four-dimensional N = 2 systems. In that context, the wall-crossing phenomenon appears as discontinuities in the value of the residue integral as the integration contour is varied. We present several examples illustrating the various aspects of the index formula.
Managing engineering systems with large state and action spaces through deep reinforcement learning

Abstract Decision-making for engineering systems management can be efficiently formulated using Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical MDP/POMDP solution procedures utilize offline knowledge about the environment and provide detailed policies for relatively small systems with tractable state and action spaces. However, in large multi-component systems the dimensions of these spaces easily explode, as system states and actions scale exponentially with the number of components, whereas environment dynamics are difficult to be described explicitly for the entire system and may, often, only be accessible through computationally expensive numerical simulators. In this work, to address these issues, an integrated Deep Reinforcement Learning (DRL) framework is introduced. The Deep Centralized Multi-agent Actor Critic (DCMAC) is developed, an off-policy actor-critic DRL algorithm that directly probes the state/belief space of the underlying MDP/POMDP, providing efficient life-cycle policies for large multi-component systems operating in high-dimensional spaces. Apart from deep network approximators parametrizing complex functions with vast state spaces, DCMAC also adopts a factorized representation of the system actions, thus being able to designate individualized component- and subsystem-level decisions, while maintaining a centralized value function for the entire system. DCMAC compares well against Deep Q-Network and exact solutions, where applicable, and outperforms optimized baseline policies that incorporate time-based, condition-based, and periodic inspection and maintenance considerations.
Progressive construction of a parametric reduced-order model for PDE-constrained optimization

Summary An adaptive approach to using reduced-order models (ROMs) as surrogates in partial differential equations (PDE)-constrained optimization is introduced that breaks the traditional offline-online framework of model order reduction. A sequence of optimization problems constrained by a given ROM is defined with the goal of converging to the solution of a given PDE-constrained optimization problem. For each reduced optimization problem, the constraining ROM is trained from sampling the high-dimensional model (HDM) at the solution of some of the previous problems in the sequence. The reduced optimization problems are equipped with a nonlinear trust-region based on a residual error indicator to keep the optimization trajectory in a region of the parameter space where the ROM is accurate. A technique for incorporating sensitivities into a reduced-order basis is also presented, along with a methodology for computing sensitivities of the ROM that minimizes the distance to the corresponding HDM sensitivity, in a suitable norm. The proposed reduced optimization framework is applied to subsonic aerodynamic shape optimization and shown to reduce the number of queries to the HDM by a factor of 4-5, compared with the optimization problem solved using only the HDM, with errors in the optimal solution far less than 0.1%. Copyright © 2014 John Wiley & Sons, Ltd.
Exponential Asymptotics for Solitons in PT-Symmetric Periodic Potentials

Solitons in one-dimensional parity-time ()-symmetric periodic potentials are studied using exponential asymptotics. The new feature of this exponential asymptotics is that, unlike conservative periodic potentials, the inner and outer integral equations arising in this analysis are both coupled systems due to complex-valued solitons. Solving these coupled systems, we show that two soliton families bifurcate out from each Bloch-band edge for either self-focusing or self-defocusing nonlinearity. An asymptotic expression for the eigenvalues associated with the linear stability of these soliton families is also derived. This formula shows that one of these two soliton families near band edges is always unstable, while the other can be stable. In addition, infinite families of -symmetric multisoliton bound states are constructed by matching the exponentially small tails from two neighboring solitons. These analytical predictions are compared with numerics. Overall agreements are observed, and minor differences explained.
CZF and Second Order Arithmetic

Abstract Constructive ZF + full separation is shown to be equiconsistent with Second Order Arithmetic.
Infinitesimal Perturbation Analysis for Personalized Cancer Therapy Design

Abstract We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under intermittent androgen suppression (IAS) to study a threshold-based policy for therapy design. IAS is currently one of the most widely used treatments for advanced prostate cancer. Patients undergoing IAS are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. One of the main challenges in IAS is to optimally design a therapy scheme, i.e., to determine when to discontinue and recommence androgen suppression. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. The threshold-based policy we propose is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. Using Infinitesimal Perturbation Analysis (IPA), we derive unbiased gradient estimators of this cost metric with respect to the controllable PSA threshold values based on actual data and show how these estimators can be used to adaptively adjust controllable parameters so as to improve therapy outcomes based on the cost metric defined.
The Children of Political Correctness

By common affirmation, a new form of political correctness has developed on college campuses over the last few years. It is said to be marked by a feeling of endangerment that supposedly comes from exposure to certain ideas. Hence, it is marked by demands for trigger warnings, safe spaces, and the like. The predominant explanation for this new PC is that today’s students have been the product of overprotective “helicopter parents,” who believe the world is a dangerous place for their children and have passed this feeling of endangerment to their children. I argue, by contrast, that what marks these students is not that they are children of helicopter parents, but that they are the children of political correctness. They have grown up within a tightly defined ideological framework that is based on certain fantasies and their options for concepts of themselves are limited to instantiations of these fantasies. Ways of seeing things that are in conflict with these fantasies therefore threaten their sense of who they are. The danger that they feel is not physical, but existential.
Multiple Penalized Principal Curves: Analysis and Computation

We study the problem of determining the one-dimensional structure that best represents a given data set. More precisely, we take a variational approach to approximating a given measure (data) by curves. We consider an objective functional whose minimizers are a regularization of principal curves and introduce a new functional which allows for multiple curves. We prove existence of minimizers and investigate their properties. While both of the functionals used are non-convex, we show that enlarging the configuration space to allow for multiple curves leads to a simpler energy landscape with fewer undesirable (high-energy) local minima. We provide an efficient algorithm for approximating minimizers of the functional and demonstrate its performance on real and synthetic data. The numerical examples illustrate the effectiveness of the proposed approach in the presence of substantial noise, and the viability of the algorithm for high-dimensional data.
A review of methodologies for natural-language-facilitated human–robot cooperation:

Natural-language-facilitated human–robot cooperation refers to using natural language to facilitate interactive information sharing and task executions with a common goal constraint between robots ...
Relaxed covariate overlap and margin‐based causal effect estimation

In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, a variety of methods based on the potential outcomes framework are used to estimate average treatment effects. One of the key assumptions is treatment positivity, which states that the probability of treatment is bounded away from zero and one for any possible combination of the confounders. Methods for performing causal inference when this assumption is violated are relatively limited. In this article, we discuss a new balance-related condition involving the convex hulls of treatment groups, which I term relaxed covariate overlap. An advantage of this concept is that it can be linked to a concept from machine learning, termed the margin. Introduction of relaxed covariate overlap leads to an approach in which one can perform causal inference in a three-step manner. The methodology is illustrated with two examples.
Locality based core selection for multicore shared tree multicasting

Multicasting can be done in two different ways: source based tree approach and shared tree approach. Protocols such as Core Based Tree (CBT), Protocol Independent Multicasting Sparse Mode (PIM-SM) use shared tree approach. Shared tree approach is preferred over source-based tree approach because in the later construction of minimum cost tree per source is needed unlike a single shared tree in the former approach. In this paper, we present a candidate core selection approach for shared tree multicasting so that in a multicast session different senders can select different cores from the candidate core set based on the senders' physical locations to allow an efficient multicore multicasting approach.
Non-divergence Parabolic Equations of Second Order with Critical Drift in Lebesgue Spaces

Abstract We consider uniformly parabolic equations and inequalities of second order in the non-divergence form with drift − u t + L u = − u t + ∑ i j a i j D i j u + ∑ b i D i u = 0 ( ≥ 0 , ≤ 0 ) in some domain Q ⊂ R n + 1 . We prove growth theorems and the interior Harnack inequality as the main results. In this paper, we will only assume the drift b is in certain Lebesgue spaces which are critical under the parabolic scaling but not necessarily to be bounded. In the last section, some applications of the interior Harnack inequality are presented. In particular, we show there is a “universal” spectral gap for the associated elliptic operator. The counterpart for uniformly elliptic equations of second order in non-divergence form is shown in [19] .
Geometric constant term functor(s)

We study the Eisenstein series and constant term functors in the framework of geometric theory of automorphic functions. Our main result says that for a parabolic \(P\subset G\) with Levi quotient M, the !-constant term functor $$\begin{aligned}{\text {CT}}_!:{\text {D-mod}}({\text {Bun}}_G)\rightarrow {\text {D-mod}}({\text {Bun}}_M)\end{aligned}$$ is canonically isomorphic to the *-constant term functor $$\begin{aligned} {\text {CT}}^-_*:{\text {D-mod}}({\text {Bun}}_G)\rightarrow {\text {D-mod}}({\text {Bun}}_M), \end{aligned}$$ taken with respect to the opposite parabolic \(P^-\).
Comparing Human and Machine Errors in Conversational Speech Transcription

Recent work in automatic recognition of conversational telephone speech (CTS) has achieved accuracy levels comparable to human transcribers, although there is some debate how to precisely quantify human performance on this task, using the NIST 2000 CTS evaluation set. This raises the question what systematic differences, if any, may be found differentiating human from machine transcription errors. In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline. We find that the most frequent substitution, deletion and insertion error types of both outputs show a high degree of overlap. The only notable exception is that the automatic recognizer tends to confuse filled pauses (“uh”) and backchannel acknowledgments (“uhhuh”). Humans tend not to make this error, presumably due to the distinctive and opposing pragmatic functions attached to these words. Furthermore, we quantify the correlation between human and machine errors at the speaker level, and investigate the effect of speaker overlap between training and test data. Finally, we report on an informal “Turing test” asking humans to discriminate between automatic and human transcription error cases.
Damage nucleation from repeated dislocation absorption at a grain boundary

Abstract Damage nucleation from repeated dislocation absorption at a grain boundary is simulated with molecular dynamics. At the grain boundary–dislocation intersection site, atomic shuffling events determine how the free volume brought by the incoming dislocation is accommodated. This process in turn determines the crack nucleation mechanism, as well as the critical strain and number of dislocations that can be absorbed before cracking. Slower strain rates promote earlier crack nucleation and a damage nucleation mode where cracking is preceded by dislocation emission. The simulation methodology presented here can be used to probe other types of boundaries as well, although multiple thermodynamically equivalent starting configurations are required to quantify the damage resistance of a given grain boundary.
Frnet: Flattened Residual Network for Infant MRI Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull strip-ping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual net-work architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.
Ensembles of kernel predictors

This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing this problem based on ensembles of kernel predictors. This includes novel theoretical guarantees based on the Rademacher complexity of the corresponding hypothesis sets, the introduction and analysis of a learning algorithm based on these hypothesis sets, and a series of experiments using ensembles of kernel predictors with several data sets. Both convex combinations of kernel-based hypotheses and more general Lq-regularized non-negative combinations are analyzed. These theoretical, algorithmic, and empirical results are compared with those achieved by using learning kernel techniques, which can be viewed as another approach for solving the same problem.
On cascade source coding with a side information “vending machine”

The model of a side information “vending machine” accounts for scenarios in which acquiring side information is costly and thus should be done efficiently. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information vending machine is available either at the intermediate or at the end node. In both cases, a single-letter characterization of the available trade-offs among the rate, the distortions in the reconstructions at the intermediate and at the end node, and the cost in acquiring the side information are derived under given conditions.
5G millimeter wave cellular system capacity with fully digital beamforming

Due to heavy reliance of millimeter-wave (mmWave) wireless systems on directional links, Beamforming (BF) with high-dimensional arrays is essential for cellular systems in these frequencies. How to perform the array processing in a power efficient manner is a fundamental challenge. Analog and hybrid BF require fewer analog-to-digital converters (ADCs), but can only communicate in a small number of directions at a time, limiting directional search, spatial multiplexing and control signaling. Digital BF enables flexible spatial processing, but must be operated at a low quantization resolution to stay within reasonable power levels. This paper presents a simple additive white Gaussian noise (AWGN) model to assess the effect of low-resolution quantization of cellular system capacity. Simulations with this model reveal that at moderate resolutions (3–4 bits per ADC), there is negligible loss in downlink cellular capacity from quantization. In essence, the low-resolution ADCs limit the high SNR, where cellular systems typically do not operate. The findings suggest that low-resolution fully digital BF architectures can be power efficient, offer greatly enhanced control plane functionality and comparable data plane performance to analog BF.
Intersection algebras for principal monomial ideals in polynomial rings

The properties of the intersection algebra of two principal monomial ideals in a polynomial ring are investigated in detail. Results are obtained regarding the Hilbert series and the canonical ideal of the intersection algebra using methods from the theory of Diophantine linear equations with integer coefficients.
A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects.

Discriminating between closely-related language varieties is considered a challenging and important task. This paper describes our submission to the DSL 2016 shared-task, which included two sub-tasks: one on discriminating similar languages and one on identifying Arabic dialects. We developed a character-level neural network for this task. Given a sequence of characters, our model embeds each character in vector space, runs the sequence through multiple convolutions with different filter widths, and pools the convolutional representations to obtain a hidden vector representation of the text that is used for predicting the language or dialect. We primarily focused on the Arabic dialect identification task and obtained an F1 score of 0.4834, ranking 6th out of 18 participants. We also analyze errors made by our system on the Arabic data in some detail, and point to challenges such an approach is faced with.
Information Geometry of Quantum Resources

I review recent works showing that information geometry is a useful framework to characterize quantum coherence and entanglement. Quantum systems exhibit peculiar properties which cannot be justified by classical physics, e.g. quantum coherence and quantum correlations. Once confined to thought experiments, they are nowadays created and manipulated by exerting an exquisite experimental control of atoms, molecules and photons. It is important to identify and quantify such quantum features, as they are deemed to be key resources to achieve supraclassical performances in computation and communication protocols. The information geometry viewpoint elucidates the advantage provided by quantum superpositions in phase estimation. Also, it enables to link measures of coherence and entanglement to observables, which can be evaluated in a laboratory by a limited number of measurements.
Changes in analysts’ stock recommendations following regulatory action against their brokerage

Despite the importance of sell-side analysts in the capital markets, we know little about the effectiveness of routine monitoring of the sell-side industry. We examine the attributes of sell-side research issued by analysts before and after their brokerage faces regulatory sanctions. We find that after a sanction, analysts at sanctioned brokerages lower their stock recommendations, both in absolute terms and relative to the recommendations of other analysts following the same firms. These analysts are also more likely than analysts at other brokerages to downgrade a company’s stock after the receipt of unfavorable information about the firm. Importantly, we document that analysts at nonsanctioned brokerages also reduce the optimism of their stock recommendations when a peer analyst’s brokerage is sanctioned, consistent with spillovers as a result of routine regulatory monitoring. Our study provides evidence that regulatory action against sell-side brokerages is associated with a reduction in sell-side analysts’ positive bias.
Congruences for coefficients of level 2 modular functions with poles at 0

We give congruences modulo powers of 2 for the Fourier coefficients of certain level 2 modular functions with poles only at 0, answering a question posed by Andersen and the first author. The congruences involve a modulus that depends on the binary expansion of the modular form’s order of vanishing at $$\infty $$ .
An online learning-based framework for tracking

We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. However, these solutions can be very sensitive to model mismatches. In this paper, motivated by online learning, we introduce a new framework for tracking. We provide an efficient tracking algorithm for this framework. We provide experimental results comparing our algorithm to the Bayesian algorithm on simulated data. Our experiments show that when there are slight model mismatches, our algorithm outperforms the Bayesian algorithm.
Automatic Response Category Combination in Multinomial Logistic Regression

We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is nondifferentiable when pa...
Poisson Group Testing: A Probabilistic Model for Boolean Compressed Sensing

We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-truncated Poisson distribution. The Poisson model has a number of new applications, including dynamic testing with diminishing relative rates of defectives. We consider both nonadaptive and semi-adaptive identification methods. For nonadaptive methods, we derive a lower bound on the number of tests required to identify the defectives with a probability of error that asymptotically converges to zero; in addition, we propose test matrix constructions for which the number of tests closely matches the lower bound. For semiadaptive methods, we describe a lower bound on the expected number of tests required to identify the defectives with zero error probability. In addition, we propose a stage-wise reconstruction algorithm for which the expected number of tests is only a constant factor away from the lower bound. The methods rely only on an estimate of the average number of defectives, rather than on the individual probabilities of subjects being defective.
PRESAGE: Protecting Structured Address Generation against Soft Errors

Modern computer scaling trends in pursuit of larger component counts and power efficiency have, unfortunately, lead to lessreliable hardware and consequently soft errors escaping intoapplication data ("silent data corruptions"). Techniques to enhance system resilience hinge on the availabilityof efficient error detectors that have high detection rates, low false positive rates, and lower computational overhead. Unfortunately, efficient detectors to detect faults duringaddress generation have not been widely researched (especially in the context of indexing large arrays). We present a novel lightweight compiler-driven technique called PRESAGE for detecting bit-flips affecting structured address computations. A key insight underlying PRESAGE is that any address computation scheme that propagates an already incurred error is better than a scheme that corrupts one particular array access but otherwise (falsely) appears to compute perfectly. Ensuring the propagation of errors allows one to place detectors at loop exit pointsand helps turn silent corruptions into easily detectable error situations. Our experiments using the PolyBench benchmark suite indicate that PRESAGE-based error detectors have a high error-detection rate while incurring low overheads.
Positive matrices in the Hardy space with prescribed boundary representations via the Kaczmarz algorithm

For a singular probability measure μ on the circle, we show the existence of positive matrices on the unit disc which admit a boundary representation on the unit circle with respect to μ. These positive matrices are constructed in several different ways using the Kaczmarz algorithm. Some of these positive matrices correspond to the projection of the Szegő kernel on the disc to certain subspaces of the Hardy space corresponding to the normalized Cauchy transform of μ. Other positive matrices are obtained which correspond to subspaces of the Hardy space after a renormalization, and so are not projections of the Szegő kernel. We show that these positive matrices are a generalization of a spectrum or Fourier frame for μ, and the existence of such a positive matrix does not require μ to be spectral.