cssi/SciSciGPT-SciSciNet huggingface dataset:
Percolation through voids around overlapping spheres: a dynamically based finite-size scaling analysis.

The percolation threshold for flow or conduction through voids surrounding randomly placed spheres is calculated. With large-scale Monte Carlo simulations, we give a rigorous continuum treatment to the geometry of the impenetrable spheres and the spaces between them. To properly exploit finite-size scaling, we examine multiple systems of differing sizes, with suitable averaging over disorder, and extrapolate to the thermodynamic limit. An order parameter based on the statistical sampling of stochastically driven dynamical excursions and amenable to finite-size scaling analysis is defined, calculated for various system sizes, and used to determine the critical volume fraction ${\ensuremath{\phi}}_{c}=0.0317\ifmmode\pm\else\textpm\fi{}0.0004$ and the correlation length exponent $\ensuremath{\nu}=0.92\ifmmode\pm\else\textpm\fi{}0.05$.
Function fields of class number one

Abstract In 1975, J. Leitzel, M. Madan and C. Queen listed 7 function fields over finite fields (up to isomorphism) with positive genus and class number one. They claimed to prove that these were the only ones such. Recently, Claudio Stirpe found an 8th one! In this paper, we fix the argument in the former paper to show that this 8th example could have been found by their method and is the only one, so that the list is now complete.
A probabilistic framework for nonlinearities in stochastic neural networks

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural networks. The framework readily integrates into existing stochastic neural networks (with hidden units characterized as random variables), allowing one for the first time to learn the nonlinearities alongside model weights in these networks. Extensive experiments demonstrate the performance improvements brought about by the proposed framework when integrated with the restricted Boltzmann machine (RBM), temporal RBM and the truncated Gaussian graphical model (TGGM).
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior

Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve this problem through projected point displacement & bundle adjustment. More recently, deep methods have attempted to solve this problem by directly learning a relationship between geometry and appearance. There is, however, a significant gap between these two strategies. SfM tackles the problem from purely a geometric perspective, taking no account of the object shape prior. Modern deep methods more often throw away geometric constraints altogether, rendering the results unreliable. In this paper we make an effort to bring these two seemingly disparate strategies together. We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.
Lescop's invariant and gauge theory

Taubes proved that the Casson invariant of an integral homology 3-sphere equals half the Euler characteristic of its instanton Floer homology. We extend this result to all closed oriented 3-manifolds with positive first Betti number by establishing a similar relationship between the Lescop invariant of the manifold and its instanton Floer homology. The proof uses surgery techniques.
A comparative analysis of state-of-the-art SQL-on-Hadoop systems for interactive analytics

Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems that are in use today. While the various SQL-on-Hadoop systems target the same class of analytical workloads, their different architectures, design decisions and implementations impact query performance. In this work, we perform a comparative analysis of four state-of-the-art SQL-on-Hadoop systems (Impala, Drill, Spark SQL and Phoenix) using the Web Data Analytics micro benchmark and the TPC-H benchmark on the Amazon EC2 cloud platform. The TPC-H experiment results show that, although Impala outperforms other systems (4.41x–6.65x) in the text format, trade-offs exists in the parquet format, with each system performing best on subsets of queries. A comprehensive analysis of execution profiles expands upon the performance results to provide insights into performance variations, performance bottlenecks and query execution characteristics.
Leveraging microgrids for capturing uncertain distribution network net load ramping

In this paper, a flexibility-oriented microgrid optimal scheduling model is proposed to mitigate distribution network net load variability caused by large penetration distributed solar generation. The distributed solar generation variability, which is caused by increasing adoption of this technology by end-use consumers, is mainly addressed by electric utilities using grid reinforcement. Microgrids, however, provide viable and local solutions to this pressing challenge. The proposed model, which is developed using mixed-integer programming and employs robust optimization, not only can efficiently capture distribution network net load variations, mainly in terms of ramping, but also accounts for possible uncertainties in forecasting. Numerical simulations on a test distribution feeder with one microgrid and several consumers/prosumers indicate the effectiveness of the proposed model.
A numerical bifurcation analysis of a dryland vegetation model

Abstract The dryland vegetation model proposed by Rietkerk and collaborators has been explored in several previous studies. Our aim here is to explore in some detail the bifurcation phenomena present when the coefficients of the model are allowed to vary in a wide range of parameters. In addition to the primary bifurcation parameter, the precipitation, we allow the two infiltration rate parameters to vary as well. We find that these two parameters control the size and stability of inhomogeneous biomass states in a way that can be predicted. Further, they control when certain homogeneous and inhomogeneous (in space) periodic (in time) orbits exist. Finally, we show that the model possesses infinitely many unphysical steady state branches. We then present a modification of the model which eliminates these unphysical solutions, and briefly explore this new model for a fixed set of parameters.
Rigorous results in existence and selection of Saffman–Taylor fingers by kinetic undercooling

The selection of Saffman–Taylor fingers by surface tension has been extensively investigated. In this paper, we are concerned with the existence and selection of steadily translating symmetric finger solutions in a Hele–Shaw cell by small but non-zero kinetic undercooling (e2). We rigorously conclude that for relative finger width λ near one half, symmetric finger solutions exist in the asymptotic limit of undercooling e2 → 0 if the Stokes multiplier for a relatively simple non-linear differential equation is zero. This Stokes multiplier S depends on the parameter and earlier calculations have shown this to be zero for a discrete set of values of α. While this result is similar to that obtained previously for Saffman–Taylor fingers by surface tension, the analysis for the problem with kinetic undercooling exhibits a number of subtleties as pointed out by Chapman and King (2003, The selection of Saffman–Taylor fingers by kinetic undercooling, Journal of Engineering Mathematics, 46, 1–32). The main subtlety is the behaviour of the Stokes lines at the finger tip, where the analysis is complicated by non-analyticity of coefficients in the governing equation.
Downsampling of optical frequency combs

We demonstrate repetition-rate downsampling of optical frequency combs by way of pulse gating. This results in a frequency comb with reduced repetition rate and increased pulse energy, which enables efficient spectral broadening and f-2f interferometry. To explore the technique, we downsample a 250 MHz repetition-rate comb to 25 MHz and detect the carrier–envelope offset frequency of the downsampled pulse train. We investigate the effects of pulse gating on the noise properties of the pulse train and the limitations of the technique by characterizing the phase-noise spectrum of the downsampled comb and deliberately imposing timing jitter on the pulse gate. We show that, up to an expected reduction modulo the new repetition rate, downsampling neither shifts nor introduces noise to the carrier–envelope offset frequency of the comb above the level of several microhertz. Additionally, we discuss the effect of downsampling on the spectrum of intensity fluctuations of the optical pulse train. Finally, we discuss some practical considerations relevant for the application of the technique to frequency combs with repetition rates in the 10 GHz range and higher.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.

Knowledge bases (KB), both automatically and manually constructed, are often incomplete — many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities, or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm, MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with unknown destination and combinatorially many paths from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. On a comprehensive evaluation on seven knowledge base datasets, we found MINERVA to be competitive with many current state-of-the-art methods.
Modular belief updates and confusion about measures of certainty in artificial intelligence research

Over the last decade, there has been growing interest in the use or measures or change in belief for reasoning with uncertainty in artificial intelligence research. An important characteristic of several methodologies that reason with changes in belief or belief updates, is a property that we term modularity. We call updates that satisfy this property modular updates. Whereas probabilistic measures of belief update - which satisfy the modularity property were first discovered in the nineteenth century, knowledge and discussion of these quantities remains obscure in artificial intelligence research. We define modular updates and discuss their inappropriate use in two influential expert systems.
Stein Variational Message Passing for Continuous Graphical Models

We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest. Our approach combines SVGD with a set of structured local kernel functions defined on the Markov blanket of each node, which alleviates the curse of high dimensionality and simultaneously yields a distributed algorithm for decentralized inference tasks. We justify our method with theoretical analysis and show that the use of local kernels can be viewed as a new type of localized approximation that matches the target distribution on the conditional distributions of each node over its Markov blanket. Our empirical results show that our method outperforms a variety of baselines including standard MCMC and particle message passing methods.
Guess & Check Codes for Deletions, Insertions, and Synchronization

We consider the problem of constructing codes that can correct $\delta $ deletions occurring in an arbitrary binary string of length $n$ bits. Varshamov–Tenengolts (VT) codes, dating back to 1965, are zero-error single deletion $(\delta =1)$ correcting codes and have an asymptotically optimal redundancy. Finding similar codes for $\delta \geq 2$ deletions remains an open problem. In this paper, we relax the standard zero-error (i.e., worst-case) decoding requirement by assuming that the positions of the $\delta $ deletions (or insertions) are independent of the code word. Our contribution is a new family of explicit codes, that we call Guess & Check (GC) codes, that can correct with high probability up to a constant number of $\delta $ deletions (or insertions). GC codes are systematic; and have deterministic polynomial time encoding and decoding algorithms. We also describe the application of GC codes to file synchronization.
Sparse generalized Fourier series via collocation-based optimization

Generalized Fourier series with orthogonal polynomial bases have useful applications in several fields, including pattern recognition and image and signal processing. However, computing the generalized Fourier series can be a challenging problem, even for relatively well behaved functions. In this paper, a method for approximating a sparse collection of Fourierlike coefficients is presented that uses a collocation technique combined with an optimization problem inspired by recent results in compressed sensing research. The discussion includes approximation error rates and numerical examples to illustrate the effectiveness of the method. One example displays the accuracy of the generalized Fourier series approximation for several test functions, while the other is an application of the generalized Fourier series approximation to rotation-invariant pattern recognition in images.
Learning in Non-convex Games with an Optimization Oracle

We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially more computation than statistical learning. In this paper we show that by slightly strengthening the oracle model, the online and the statistical learning models become computationally equivalent. Our result holds for any Lipschitz and bounded (but not necessarily convex) function. As an application we demonstrate how the offline oracle enables efficient computation of an equilibrium in non-convex games, that include GAN (generative adversarial networks) as a special case.
Task-Driven Estimation and Control via Information Bottlenecks

Our goal is to develop a principled and general algorithmic framework for task-driven estimation and control for robotic systems. State-of-the-art approaches for controlling robotic systems typically rely heavily on accurately estimating the full state of the robot (e.g., a running robot might estimate joint angles and velocities, torso state, and position relative to a goal). However, full state representations are often excessively rich for the specific task at hand and can lead to significant computational inefficiency and brittleness to errors in state estimation. In contrast, we present an approach that eschews such rich representations and seeks to create task-driven representations. The key technical insight is to leverage the theory of information bottlenecks to formalize the notion of a “task-driven representation” in terms of information theoretic quantities that measure the minimality of a representation. We propose novel iterative algorithms for automatically synthesizing (offline) a task-driven representation (given in terms of a set of task-relevant variables (TRVs)) and a performant control policy that is a function of the TRVs. We presentonline algorithms for estimating the TRVs in order to apply the control policy. We demonstrate that our approach results in significant robustness to unmodeled measurement uncertainty both theoretically and via thorough simulation experiments including a spring-loaded inverted pendulum running to a goal location.
Deep Reinforcement Learning for Sequence-to-Sequence Models

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide state-of-the-art performance in a wide variety of tasks, such as machine translation, headline generation, text summarization, speech-to-text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder–decoder models produce competitive results, many researchers have proposed additional improvements over these seq2seq models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with seq2seq models that enable remembering long-term memories. We present some of the most recent frameworks that combine the concepts from RL and deep neural networks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization and provide some targeted experiments for these RL models, both in terms of performance and training time.
How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages

We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative fi...
Single-cell profiling guided combinatorial immunotherapy for fast-evolving CDK4/6 inhibitor-resistant HER2-positive breast cancer.

Acquired resistance to targeted cancer therapy is a significant clinical challenge. In parallel with clinical trials combining CDK4/6 inhibitors to treat HER2+ breast cancer, we sought to prospectively model tumor evolution in response to this regimen in vivo and identify a clinically actionable strategy to combat drug resistance. Despite a promising initial response, acquired resistance emerges rapidly to the combination of anti-HER2/neu antibody and CDK4/6 inhibitor Palbociclib. Using high-throughput single-cell profiling over the course of treatments, we reveal a distinct immunosuppressive immature myeloid cell (IMC) population to infiltrate the resistant tumors. Guided by single-cell transcriptome analysis, we demonstrate that combination of IMC-targeting tyrosine kinase inhibitor cabozantinib and immune checkpoint blockade enhances anti-tumor immunity, and overcomes the resistance. Furthermore, sequential combinatorial immunotherapy enables a sustained control of the fast-evolving CDK4/6 inhibitor-resistant tumors. Our study demonstrates a translational framework for treating rapidly evolving tumors through preclinical modeling and single-cell analyses. The benefit of combined CDK4/6 and anti-HER2 therapy in breast cancer is limited due to acquired resistance. Here, the authors perform single-cell analysis and show an immature myeloid cell population to infiltrate resistant tumors, and that combined cabozantinib and checkpoint therapy overcome this resistance with a sustained efficacy.
Ricci flow with surgery on manifolds with positive isotropic curvature

We study the Ricci flow for initial metrics with positive isotropic curvature (strictly PIC for short). In the first part of this paper, we prove new curvature pinching estimates which ensure that blow-up limits are uniformly PIC in all dimensions. Moreover, in dimension $n \geq 12$, we show that blow-up limits are weakly PIC2. This can be viewed as a higher-dimensional version of the fundamental Hamilton-Ivey pinching estimate in dimension $3$. In the second part, we develop a theory of ancient solutions which have bounded curvature; are $\kappa$-noncollapsed; are weakly PIC2; and are uniformly PIC. This is an extension of Perelman's work; the additional ingredients needed in the higher dimensional setting are the differential Harnack inequality for solutions to the Ricci flow satisfying the PIC2 condition, and a rigidity result due to Brendle-Huisken-Sinestrari for ancient solutions that are uniformly PIC1. In the third part of this paper, we prove a Canonical Neighborhood Theorem for the Ricci flow with initial data with positive isotropic curvature, which holds in dimension $n \geq 12$. This relies on the curvature pinching estimates together with the structure theory for ancient solutions. This allows us to adapt Perelman's surgery procedure to this situation. As a corollary, we obtain a topological classification of all compact manifolds with positive isotropic curvature of dimension $n \geq 12$ which do not contain non-trivial incompressible $(n-1)$-dimensional space forms.
Investment income taxes and private equity acquisition activity

Abstract Utilizing a novel identification strategy, we uncover evidence that reducing the investment income tax rate increases acquisition activity by private equity acquirers. Applying a difference-in-difference methodology, we find that acquisitions sponsored by private equity firms nearly doubled following the investment income tax rate reductions associated with the Taxpayer Relief Act of 1997 and the Jobs and Growth Tax Relief Reconciliation Act of 2003. We attribute our findings to private equity fund structures and managing partners’ ability to capture the expected benefit of lower capital gains tax rates. These findings are robust to controlling for target shareholders’ tax incentives, as well as firm, industry, and macroeconomic factors possibly influencing acquisition activity.
On the CR transversality of holomorphic maps into hyperquadrics

Let M l be a smooth Levi-nondegenerate hypersurface of signature l in C n with n ≥ 3, and write H l N for the standard hyperquadric of the same signature in C N with \(N - n <\frac{n-1} {2}\). Let F be a holomorphic map sending M l into \(H_{\ell}^{N}\). Assume F does not send a neighborhood of M l in C n into \(H_{\ell}^{N}\). We show that F is necessarily CR transversal to M l at any point. Equivalently, we show that F is a local CR embedding from M l into \(H_{\ell}^{N}\).
DLHub: Model and Data Serving for Science

While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications. DLHub addresses two significant shortcomings in current systems. First, its self-service model repository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published models through a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3-compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications.
COMPLEXITY PROBLEMS IN ENUMERATIVE COMBINATORICS

We give a broad survey of recent results in Enumerative Combinatorics and their complexity aspects.
Identifying Parameter Space for Robust Stability in Nonlinear Networks: A Microgrid Application

As modern engineering systems grow in complexity, attitudes toward a modular design approach become increasingly more favorable. A key challenge to a modular design approach is the certification of robust stability under uncertainties in the rest of the network. In this paper, we consider the problem of identifying the parametric region, which guarantees stability of the connected module in the robust sense under uncertainties. We derive the conditions under which the robust stability of the connected module is guaranteed for some values of the design parameters, and present a sum-of-squares (SOS) optimization-based algorithm to identify such a parametric region for polynomial systems. Using the example of an inverter-based microgrid, we show how this parametric region changes with variations in the level of uncertainties in the network.
Peroxisome turnover and diurnal modulation of antioxidant activity in retinal pigment epithelia utilizes microtubule-associated protein 1 light chain 3B (LC3B).

The retinal pigment epithelium (RPE) supports the outer retina through essential roles in the retinoid cycle, nutrient supply, ion exchange, and waste removal. Each day the RPE removes the oldest ~...
Hybrid-Beamforming-Based Millimeter-Wave Cellular Network Optimization

Massive MIMO and millimeter-wave communication (mmWave) have recently emerged as two key technologies for building 5G wireless networks and beyond. To reconcile the conflict between the large antenna arrays and the limited amount of radio-frequency (RF) chains in mmWave systems, the so-called hybrid beamforming becomes a promising solution and has received a great deal of attention in recent years. However, existing research on hybrid beamforming focused mostly on the physical layer or signal processing aspects. So far, there is a lack of theoretical understanding of how hybrid beamforming could affect mmWave network optimization . In this paper, we consider the impacts of hybrid beamforming on utility-optimality and queuing delay in mmWave cellular network optimization. Our contributions in this paper are three-fold: i) we develop a joint hybrid beamforming and congestion control algorithmic framework for mmWave network utility maximization; ii) we reveal a pseudoconvexity structure in the hybrid beamforming scheduling problem, which leads to simplified analog beamforming protocol design; and iii) we theoretically characterize the scalings of utility-optimality and delay with respect to channel state information (CSI) accuracy in digital beamforming.
Linking topological spheres

There is a topological embedding $\iota:\mathbb{S}^1\to\mathbb{R}^5$ such that $\pi_3(\mathbb{R}^5\setminus\iota(\mathbb{S}^1))=0$. Therefore, no $3$-sphere can be linked with $\iota(\mathbb{S}^1)$.
Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7~to~17$\times$ lower inference runtime cost (11~to~26$\times$ in FLOPs), even when the target video's distribution is non-stationary. Our method requires no offline pretraining on the target video stream, achieves higher accuracy and lower cost than solutions based on flow or video object segmentation, and can exhibit better temporal stability than the original teacher. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.
“In-memory Computing”: Accelerating AI Applications

Recent years have witnessed a tremendous explosion of data, which continues unabatedly: It is estimated that the digital universe is growing at a rate of about 60% per year. All this data significantly improves our understanding of today's incredibly complex economies and societies. Moreover, it ushers in a new era of computing, namely, the cognitive or AI era, in which the data is considered a new natural resource.
Deep Reinforcement Learning for Task-Driven Discovery of Incomplete Networks

Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem in an incomplete network setting as a sequential decision making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called Network Actor Critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. A quantitative study is presented on several synthetic and real benchmarks. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms.
Processor-Oblivious Record and Replay

Record-and-replay systems are useful tools for debugging non-deterministic parallel programs by first recording an execution and then replaying that execution to produce the same access pattern. Existing record-and-replay systems generally target thread-based execution models, and record the behaviors and interleavings of individual threads. Dynamic multithreaded languages and libraries, such as the Cilk family, OpenMP, TBB, and the like, do not have a notion of threads. Instead, these languages provide a processor-oblivious model of programming, where programs expose task parallelism using high-level constructs such as spawn/sync without regard to the number of threads/cores available to run the program. Thread-based record-and-replay would violate the processor-oblivious nature of these programs, as they incorporate the number of threads into the recorded information, constraining the replayed execution to the same number of threads. In this article, we present a processor-oblivious record-and-replay scheme for dynamic multithreaded languages where record and replay can use different number of processors and both are scheduled using work stealing. We provide theoretical guarantees for our record and replay scheme—namely that record is optimal for programs with one lock and replay is near-optimal for all cases. In addition, we implemented this scheme in the Cilk Plus runtime system and our evaluation indicates that processor-obliviousness does not cause substantial overheads.
Seasonal and directional dispersal behavior in an ongoing dove invasion

Range expansions require the dispersal of individual organisms, but dispersal behavior is notoriously difficult to study. Eurasian collared‐doves have colonized both Europe and North America this century, with both initial invasions proceeding northwestward via ‘jump’ dispersal. The European population has reached carrying capacity, but in the Americas, where exponential population growth continues, little is known about dispersal behavior. I queried citizen science field notes to investigate Eurasian collared‐dove dispersal behavior in North America along the Pacific Ocean, a natural barrier to northwestward landbird dispersal. I examined coastal and pelagic records of Eurasian collared‐doves to determine the seasonality and directionality of dispersal flights and to assess changes in the frequency of dispersal‐related behavior across years. Most reports of Eurasian collared‐doves flying in flocks along the coast and most pelagic sightings were in March–May, mirroring the spring natal dispersal documented during the European invasion. Almost all spring coastal flocks were flying north, consistent with the overall northwestward direction of the initial North American invasion and northward deflection of northwest‐bound birds upon encountering the Pacific Ocean. Spring flying flock observations showed no significant change in frequency from 2010 to 2018 after controlling for overall increases in spring collared‐dove reports. This refined understanding of Eurasian collared‐dove dispersal behavior informs full life cycle management of this rapidly increasing invader in the Americas, raises new questions about evolutionary mechanisms driving this spectacular invasion, and illustrates the potential power of citizen science field notes for documenting dispersal behavior, a longstanding challenge in ecology.
Generative Speech Enhancement Based on Cloned Networks

We propose to implement speech enhancement by the regeneration of clean speech from a ‘salient’ representation extracted from the noisy signal. The network that extracts salient features is trained using a set of weight-sharing clones of the extractor network. The clones receive mel-frequency spectra of different noisy versions of the same speech signal as input. By encouraging the outputs of the clones to be similar for these different input signals, we train a feature extractor network that is robust to noise. At inference, the salient features form the input to a WaveNet network that generates a natural and clean speech signal with the same attributes as the ground-truth clean signal. As the signal becomes noisier, our system produces natural sounding errors that stay on the speech manifold, in place of traditional artifacts found in other systems. Our experiments confirm that our generative enhancement system provides state-of-the-art enhancement performance within the generative class of enhancers according to a MUSHRA-like test. The clones based system matches or outperforms the other systems at each input signal-to-noise (SNR) range with statistical significance.
Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.
The corank of a rectangular random integer matrix

Abstract We show that under reasonable conditions, a random n × ( 2 + ϵ ) n integer matrix is surjective on Z n with probability 1 − O ( e − c n ) . We also conjecture that this should hold for n × ( 1 + ϵ ) n , and provide a counterexample to show that our “reasonableness” conditions are necessary.
Disposal is Not Free: Fiscal Instruments to Internalize the Environmental Costs of Solid Waste

This paper provides an overview of global solid waste generation, its environmental costs, and fiscal instruments that can be used to encourage waste reduction and finance proper disposal. Countries—especially island nations--struggle to manage an ever-increasing volume of solid waste, generation of which is projected to exceed 2 billion tons a year by 2025. Although solid waste management is usually relegated to subnational governments, externalities from inadequate management, which include greenhouse gas emissions and ocean plastic pollution, reach global scale. National governments thus play a critical role in creating incentives for waste minimization and ensuring adequate resources for proper waste management. This paper evaluates potential fiscal instruments to achieve these goals, particularly in developing country policy environments.
The Dunkl weight function for rational Cherednik algebras

In this paper we prove the existence of the Dunkl weight function $$K_{c, \lambda }$$ for any irreducible representation $$\lambda $$ of any finite Coxeter group W, generalizing previous results of Dunkl. In particular, $$K_{c, \lambda }$$ is a family of tempered distributions on the real reflection representation of W taking values in $$\text {End}_\mathbb {C}(\lambda )$$, with holomorphic dependence on the complex multi-parameter c. When the parameter c is real, the distribution $$K_{c, \lambda }$$ provides an integral formula for Cherednik’s Gaussian inner product $$\gamma _{c, \lambda }$$ on the Verma module $$\Delta _c(\lambda )$$ for the rational Cherednik algebra $$H_c(W, \mathfrak {h})$$.queryPlease check and confirm the inserted city name ‘Stanford’ for the affiliation is correct. In this case, the restriction of $$K_{c, \lambda }$$ to the hyperplane arrangement complement $$\mathfrak {h}_{\mathbb {R}, reg}$$ is given by integration against an analytic function whose values can be interpreted as braid group invariant Hermitian forms on $$KZ(\Delta _c(\lambda ))$$, where KZ denotes the Knizhnik–Zamolodchikov functor introduced by Ginzburg–Guay–Opdam–Rouquier. This provides a concrete connection between invariant Hermitian forms on representations of rational Cherednik algebras and invariant Hermitian forms on representations of Iwahori–Hecke algebras, and we exploit this connection to show that the KZ functor preserves signatures, and in particular unitarizability, in an appropriate sense.
Some Linear Recurrences Motivated by Stern’s Diatomic Array

AbstractWe define a triangular array closely related to Stern’s diatomic array and show that for a fixed integer r≥1 , the sum ur(n) of the rth powers of the entries in row n satisfy a linear recur...
Manipulation of perceived politeness in a web-based email discourse through a malicious browser extension

This paper presents a specific man-in-the-middle exploit: Ambient Tactical Deception (ATD) in online communication, realized via a malicious web browser extension. Extensions manipulate web content in unobtrusive ways as ambient intermediaries of the overall browsing experience. In our previous work, we demonstrated that it is possible to employ tactical deception by making covert changes in the text content of a web page, regardless of the source. In this work, we investigated the application of ATD in a web-based email discourse where the objective is to manipulate the interpersonal perception without the knowledge of the involved parties. We focus on web-based email text because it is asynchronous and usually revised for clarity and politeness. Previous research has demonstrated that people's perception of politeness in online communication is based on three factors: the degree of imposition, the power of the receiver over the sender, and the social distance between them. We interviewed participants about their perception of these factors to establish the plausibility of ATD for email discourse. The results indicate that by covertly altering the politeness strategy in an email, it is possible for an ATD attacker to manipulate the receiver's perception on all of the politeness factors. Our findings support the Brown and Levinson's politeness theory and Walther's hyperpersonal model of email communication.
A Systematic Review of Digital vs Analog Drainage for Air Leak After Surgical Resection or Spontaneous Pneumothorax

Background The concerns regarding air leak after lung surgery or spontaneous pneumothorax include detection and duration. Prior studies have suggested that digital drainage systems permit shorter chest tube duration and hospital length of stay (LOS) by earlier detection of air leak cessation. We conducted a systematic review to assess the impact of digital drainage on chest tube duration and hospital LOS after pulmonary surgery and spontaneous pneumothorax. Methods Ovid MEDLINE, PubMed, Embase, the Cochrane Library, Scopus, and Google Scholar were searched from inception through January 2019. We included randomized controlled trials, cohort studies, and case series of adult patients, using digital or traditional drainage devices for air leaks of either postsurgical or spontaneous pneumothorax origin. Results Of 1,272 references reviewed, 23 articles were included. Nineteen articles addressed postoperative air leak, and four articles pertained to air leak after spontaneous pneumothorax. Thirteen studies were randomized controlled trials. Digital drainage resulted in significantly shorter chest tube duration in eight of 18 studies and shorter hospital LOS in six of 14 studies for postoperative air leak. For postpneumothorax air leak, digital drainage resulted in significantly shorter chest tube duration in two of three studies and hospital LOS in one of two studies with an analog control group. Conclusions Most studies show no significant differences in chest tube duration and hospital LOS with digital vs analog drainage systems for patients with air leak after pulmonary resection. For post-spontaneous pneumothorax air leak, the limited published evidence suggests shorter chest tube duration and hospital LOS with digital drainage systems.
The Genomic Schur Function is Fundamental-Positive

In work with A. Yong, the author introduced genomic tableaux to prove the first positive combinatorial rule for the Littlewood–Richardson coefficients in torus-equivariant K-theory of Grassmannians. We then studied the genomic Schur function $$U_\lambda $$, a generating function for such tableaux, showing that it is nontrivially a symmetric function, although generally not Schur-positive. Here, we show that $$U_\lambda $$ is, however, positive in the basis of fundamental quasisymmetric functions. We give a positive combinatorial formula for this expansion in terms of gapless increasing tableaux; this is, moreover, the first finite expression for $$U_\lambda $$. Combined with work of A. Garsia and J. Remmel, this yields a compact combinatorial (but necessarily nonpositive) formula for the Schur expansion of $$U_\lambda $$.
Achieving the Bayes Error Rate in Synchronization and Block Models by SDP, Robustly

We study the statistical performance of semidefinite programming (SDP) relaxations for clustering under random graph models. Under the $\mathbb {Z}_{2}$ Synchronization model, Censored Block Model (CBM) and Stochastic Block Model (SBM), we show that SDP achieves an error rate of the form $\exp [-(1-o(1)) \bar { n}{ {I}^{*}}]$ . Here $ \bar { n}$ is an appropriate multiple of the number of nodes and ${ {I}^{*}}$ is an information-theoretic measure of the signal-to-noise ratio. We provide matching lower bounds on the Bayes error for each model and therefore demonstrate that the SDP approach is Bayes optimal. As a corollary, our results imply that SDP achieves the optimal exact recovery threshold under each model. Furthermore, we show that SDP is robust: the above bound remains valid under semirandom versions of the models in which the observed graph is modified by a monotone adversary. Our proof is based on a novel primal-dual analysis of SDP under a unified framework for all three models, and the analysis shows that SDP tightly approximates a joint majority voting procedure.
Whole-proteome tree of life suggests a deep burst of organism diversity

An organism tree of life (organism ToL) is a conceptual and metaphorical tree to capture a simplified narrative of the evolutionary course and kinship among the extant organisms. Such a tree cannot be experimentally validated but may be reconstructed based on characteristics associated with the organisms. Since the whole-genome sequence of an organism is, at present, the most comprehensive descriptor of the organism, a whole-genome sequence-based ToL can be an empirically derivable surrogate for the organism ToL. However, experimentally determining the whole-genome sequences of many diverse organisms was practically impossible until recently. We have constructed three types of ToLs for diversely sampled organisms using the sequences of whole genome, of whole transcriptome, and of whole proteome. Of the three, whole-proteome sequence-based ToL (whole-proteome ToL), constructed by applying information theory-based feature frequency profile method, an "alignment-free" method, gave the most topologically stable ToL. Here, we describe the main features of a whole-proteome ToL for 4,023 species with known complete or almost complete genome sequences on grouping and kinship among the groups at deep evolutionary levels. The ToL reveals 1) all extant organisms of this study can be grouped into 2 "Supergroups," 6 "Major Groups," or 35+ "Groups"; 2) the order of emergence of the "founders" of all of the groups may be assigned on an evolutionary progression scale; 3) all of the founders of the groups have emerged in a "deep burst" at the very beginning period near the root of the ToL-an explosive birth of life's diversity.