cssi/SciSciGPT-SciSciNet huggingface dataset:
Efficient synthesis of unsymmetrical 1,4-disubstituted-2,3-diketopiperazines via tandem reductive amination–cyclization

Abstract Herein we report the efficient syntheses of 1,4-disubstituted-2,3-diketopiperazines and 1,4,5-trisubstituted-2,3-diketopiperazines, which feature a tandem reductive amination and acylation. Aliphatic and aromatic primary amines serve as viable nucleophiles under the mild reaction conditions.
Copper-Catalyzed Multicomponent Cascade Process for the Synthesis of Hexahydro-1H-isoindolones

Copper-catalyzed coupling of imines, dienylstannanes, and acryloyl chlorides followed by a Diels−Alder reaction afforded hexahydro-1H-isoindolones. Diversification of the core via Pd-catalyzed cros...
The 2019 Update of the Health Equity and Financial Protection Indicators Database : An Overview

This paper outlines changes that have been made in the 2019 version of the Health Equity and Financial Protection Indicators database. On the financial protection side, the changes include an increase in the number of indicators from five to 14; revisions to several previous data points, reflecting the analysis of new surveys (or adaptations thereof); and refinements to the estimation of out-of-pocket expenditures. On the health equity side, the 2019 database includes 198 more data points than the 9,733 in the 2018 database, reflecting the addition of 535 new datapoints, and the dropping of 337 previously included data points now considered to be substandard.
Synthesis of aziridinomitosenes through base-catalyzed conjugate addition

Synthesis of an aziridinomitosene core structure that relies on a facile tertiary-amine base-catalyzed azide conjugate addition is reported. Straightforward derivatization of the conjugate addition product affords the desired mitomycin ring system. Initial catalyst screens have identified peptides that afford the product with modest enantioselectivities.
Oxidation of carbamate-protected alkylhydrazines to the corresponding hydrazones under Swern conditions

Abstract Carbamate protected alkylhydrazines have been found to oxidize cleanly to the corresponding hydrazone under Swern conditions. The reaction works on hydrazines with primary, secondary, and branched alkyl groups.
HINGE: long-read assembly achieves optimal repeat resolution

Long-read sequencing technologies have the potential to produce gold-standard de novo genome assemblies, but fully exploiting error-prone reads to resolve repeats remains a challenge. Aggressive approaches to repeat resolution often produce misassemblies, and conservative approaches lead to unnecessary fragmentation. We present HINGE, an assembler that seeks to achieve optimal repeat resolution by distinguishing repeats that can be resolved given the data from those that cannot. This is accomplished by adding "hinges" to reads for constructing an overlap graph where only unresolvable repeats are merged. As a result, HINGE combines the error resilience of overlap-based assemblers with repeat-resolution capabilities of de Bruijn graph assemblers. HINGE was evaluated on the long-read bacterial data sets from the NCTC project. HINGE produces more finished assemblies than Miniasm and the manual pipeline of NCTC based on the HGAP assembler and Circlator. HINGE also allows us to identify 40 data sets where unresolvable repeats prevent the reliable construction of a unique finished assembly. In these cases, HINGE outputs a visually interpretable assembly graph that encodes all possible finished assemblies consistent with the reads, while other approaches such as the NCTC pipeline and FALCON either fragment the assembly or resolve the ambiguity arbitrarily.
A new bipyrrole coupling reaction

Pyrrole 8 was converted to 10 in 4 steps, and 10 was coupled directly to bipyrroles 1 in a Sonogashira reaction, respectively at the 3,3′ positions.
High-dose Cholecalciferol Supplementation in Adults with Cystic Fibrosis.

Introduction Despite the availability of consensus guidelines for the treatment of vitamin D deficiency, prospective trials are lacking to examine alternative dosing strategies for adult patients with cystic fibrosis (CF) who do not meet therapeutic goals with standard regimens. Objectives The primary objective of this study was to determine the efficacy of high-dose cholecalciferol supplementation in increasing serum vitamin D (25-OHD) levels in adult patients with CF. Methods Patients were eligible for inclusion if they were 18 years or older, had baseline 25-OHD levels lower than 30 ng/ml, and were diagnosed with CF and pancreatic insufficiency. Patients were given a single dose of cholecalciferol 300,000 or 500,000 IU based on baseline 25-OHD levels. Response was defined by 25-OHD and ionized calcium levels at 3 months. At 6 months, responders received a second dose of the same strength, and nonresponders were given a weekly supplement of cholecalciferol 50,000 IU in addition to cholecalciferol 500,000 IU. A second 25-OHD level was obtained at 9 months. Results Of the 46 patients enrolled, 32 patients (70%) completed the study. Baseline levels of 25-OHD significantly increased over time in the per protocol population at 3 and 9 months. A total of 16 patients (50%) were considered nonresponders and required weekly supplementation. Conclusion A protocol using high-dose cholecalciferol or high-dose plus weekly cholecalciferol is safe and effective in treating adult patients with CF and pancreatic insufficiency.
Motivic strict ring spectra representing semi-topological cohomology theories

We show that Shipley's "detection functor" for symmetric spectra generalizes to motivic symmetric spectra. As an application, we construct motivic strict ring spectra representing morphic cohomology, semi-topological $K$-theory, and semi-topological cobordism for complex varieties. As a further application to semi-topological cobordism, we show that it is related to semi-topological $K$-theory via a Conner-Floyd type isomorphism and that after inverting a lift of the Friedlander-Mazur $s$-element in morphic cohomology, semi-topological cobordism becomes isomorphic to periodic complex cobordism.
On stable rationality of Fano threefolds and del Pezzo fibrations

We prove that very general non-rational Fano threefolds which are not birational to cubic threefolds are not stably rational.
Improved detection of changes in species richness in high diversity microbial communities

High throughput sequencing (HTS) continues to expand our understanding of microbial communities, despite insufficient sequencing depths to detect all rare taxa. These low abundance taxa are not accounted for in existing methods for detecting changes in species richness. We address this with a new hierarchical model that permits rigorous testing for both heterogeneity and biodiversity changes, and simultaneously improves Type I & II error rates compared to existing methods.
A Lyapunov and Sacker–Sell spectral stability theory for one-step methods

Approximation theory for Lyapunov and Sacker–Sell spectra based upon QR techniques is used to analyze the stability of a one-step method solving a time-dependent (nonautonomous) linear ordinary differential equation (ODE) initial value problem in terms of the local error. Integral separation is used to characterize the conditioning of stability spectra calculations. The stability of the numerical solution by a one-step method of a nonautonomous linear ODE using real-valued, scalar, nonautonomous linear test equations is justified. This analysis is used to approximate exponential growth/decay rates on finite and infinite time intervals and establish global error bounds for one-step methods approximating uniformly, exponentially stable trajectories of nonautonomous and nonlinear ODEs. A time-dependent stiffness indicator and a one-step method that switches between explicit and implicit Runge–Kutta methods based upon time-dependent stiffness are developed based upon the theoretical results.
Erasure Coding for Distributed Matrix Multiplication for Matrices With Bounded Entries

Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slowest workers (called stragglers). Recent work has demonstrated that straggler mitigation can be viewed as a problem of designing erasure codes. For matrices A and B, the technique essentially maps the computation of $\text {A}^{T} \text {B}$ into the multiplication of smaller (coded) submatrices. The stragglers are treated as erasures in this process. The computation can be completed as long as a certain number of workers (called the recovery threshold) complete their assigned tasks. We present a novel coding strategy for this problem when the absolute values of the matrix entries are sufficiently small. We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold. At one extreme, we are optimal with respect to the recovery threshold, and on the other extreme, we match the threshold of prior work. Experimental results on cloud-based clusters validate the benefits of our method.
Real-Time Feedback-Based Optimization of Distribution Grids: A Unified Approach

This paper develops an algorithmic framework for real-time optimization of distribution-level distributed energy resources (DERs). The framework optimizes the operation of DERs that are individually controllable as well as groups of DERs that are jointly controlled at an electrical point of connection. From an electrical standpoint, wye and delta single-phase and multiphase connections are accounted for. The algorithm enables DERs to pursue given performance objectives, while adjusting their powers to respond to services requested by grid operators and to maintain electrical quantities within engineering limits. The design of the algorithm leverages a time-varying bilevel problem formulation, capturing various performance objectives and engineering constraints, and an online implementation of primal-dual projected-gradient methods. The gradient steps are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. The resulting algorithm can cope with inaccuracies in the distribution-system modeling; moreover, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Analytical stability and convergence claims are established in terms of tracking the solution of the formulated time-varying optimization problem. The proposed method is tested in a realistic distribution system by using real data.
Constructing tame supercuspidal representations

A new approach to Jiu-Kang Yu's construction of tame supercuspidal representations of $p$-adic reductive groups is presented. Connections with the theory of cuspidal Deligne-Lusztig representations of finite groups of Lie type are also discussed.
On the Lazarev-Lieb extension of the Hobby-Rice theorem

Abstract O. Lazarev and E.H. Lieb proved that, given f 1 , … , f n ∈ L 1 ( [ 0 , 1 ] ; C ) , there exists a smooth function Φ that takes values on the unit circle and annihilates span { f 1 , … , f n } . We give an alternative proof of that fact that also shows the W 1 , 1 norm of Φ can be bounded by 5 π n + 1 . Answering a question raised by Lazarev and Lieb, we show that if p > 1 then there is no bound for the W 1 , p norm of any such multiplier in terms of the norms of f 1 , … , f n .
Étoiles and valuations

Abstract We establish some properties of etoiles and associated valuations over complex analytic spaces, establishing that Abhyankar's inequality holds. We give some examples of pathological behavior of these valuations. We prove a regularization theorem for complex analytic morphisms. The property of a morphism being regular and the regularization of a morphism play a major role in this theory.
Stochastic Gradient Monomial Gamma Sampler

Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A generalized kinetic function is leveraged, delivering superior stationary mixing, especially for multimodal distributions. Techniques are also discussed to overcome the practical issues introduced by this generalization. It is shown that the proposed approach is better at exploring complex multimodal posterior distributions, as demonstrated on multiple applications and in comparison with other stochastic gradient MCMC methods.
Vicious Circle Principle and Formation of Sets in ASP Based Languages

The paper continues the investigation of Poincare and Russel’s Vicious Circle Principle (VCP) in the context of the design of logic programming languages with sets. We expand previously introduced language \(\mathcal {A}log\) with aggregates by allowing infinite sets and several additional set related constructs useful for knowledge representation and teaching. In addition, we propose an alternative formalization of the original VCP and incorporate it into the semantics of new language, \(\mathcal {S}log^+\), which allows more liberal construction of sets and their use in programming rules. We show that, for programs without disjunction and infinite sets, the formal semantics of aggregates in \(\mathcal {S}log^+\) coincides with that of several other known languages. Their intuitive and formal semantics, however, are based on quite different ideas and seem to be more involved than that of \(\mathcal {S}log^+\).
A Delayed Yule Process

In now classic work, David Kendall (1966) recognized that the Yule process and Poisson process could be related by a (random) time change. Furthermore, he showed that the Yule population size rescaled by its mean has an almost sure exponentially distributed limit as $t\to \infty$. In this note we introduce a class of coupled delayed Yule processes parameterized by $0 < \alpha \le 1$ that includes the Poisson process at $\alpha = {1/2}$. Moreover we extend Kendall's limit theorem to include a larger class of positive martingales derived from functionals that gauge the population genealogy. A somewhat surprising connection with the Holley-Liggett smoothing transformation also emerges in this context. Specifically, the latter is exploited to uniquely characterize the moment generating functions of distributions of the limit martingales, generalizing Kendall's mean one exponential limit.
XONN: XNOR-based oblivious deep neural network inference

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN (pronounced /z∧n/), a novel end-to-end framework based on Yao's Garbled Circuits (GC) protocol, that provides a paradigm shift in the conceptual and practical realization of oblivious inference. In XONN, the costly matrixmultiplication operations of the deep learning model are replaced with XNOR operations that are essentially free in GC. We further provide a novel algorithm that customizes the neural network such that the runtime of the GC protocol is minimized without sacrificing the inference accuracy. We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. We further provide a compiler to translate the model description from high-level Python (i.e., Keras) to that of XONN. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7×, MiniONN (ACM CCS'17) by 93×, and SecureML (IEEE S&P'17) by 37×. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis. The datasets include breast cancer, diabetes, liver disease, and Malaria.
A Priori Estimates for the Compressible Euler Equations for a Liquid with Free Surface Boundary and the Incompressible Limit

In this paper, we prove a new type of energy estimates for the compressible Euler's equation with free boundary, with a boundary part and an interior part. These can be thought of as a generalization of the energies in Christodoulou and Lindblad [CL] to the compressible case and do not require the fluid to be irrotational. In addition, we show that our estimates are in fact uniform in the sound speed. As a consequence, we obtain convergence of solutions of compressible Euler equations with a free boundary to solutions of the incompressible equations, generalizing the result of Ebin [Eb] to when you have a free boundary. In the incompressible case our energies reduces to those in [CL] and our proof in particular gives a simplified proof of the estimates in [CL] with improved error estimates. Since for an incompressible irrotational liquid with free surface there are small data global existence results our result leaves open the possibility of long time existence also for slightly compressible liquids with a free surface.
MMD GAN: Towards Deeper Understanding of Moment Matching Network

Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing {\it adversarial kernel learning} techniques, as the replacement of a fixed Gaussian kernel in the original GMMN. The new approach combines the key ideas in both GMMN and GAN, hence we name it MMD-GAN. The new distance measure in MMD-GAN is a meaningful loss that enjoys the advantage of weak$^*$ topology and can be optimized via gradient descent with relatively small batch sizes. In our evaluation on multiple benchmark datasets, including MNIST, CIFAR-10, CelebA and LSUN, the performance of MMD-GAN significantly outperforms GMMN, and is competitive with other representative GAN works.
Synthesizing Omnidirectional Antenna Patterns, Received Power and Path Loss from Directional Antennas for 5G Millimeter-Wave Communications

Omnidirectional path loss models are vital for radiosystem design in wireless communication systems, as they allow engineers to perform network simulations for systems with arbitrary antenna patterns. At millimeter-wave frequencies, channel measurements are frequently conducted using steerable highgain directional antennas at both the transmitter and receiver to make up for the significant increase in free space path loss at these frequencies compared to traditional cellular systems that operate at lower frequencies. The omnidirectional antenna pattern, and resulting omnidirectional received power must therefore be synthesized from many unique pointing angles, where the transmit and receive antennas are rotated over many different azimuth and elevation planes. In this paper, the equivalent omnidirectional antenna pattern and omnidirectional received power are synthesized by summing the received powers from all measured unique pointing angles obtained at antenna halfpower beamwidth step increments in the azimuth and elevation planes, and this method is validated by demonstrating that the synthesized omnidirectional received power and path loss are independent of antenna beamwidth, through theoretical analyses and millimeter-wave propagation measurements using antennas with different beamwidths. The method in this paper is shown to provide accurate results while enhancing the measurement range substantially through the use of directional antennas.
Special L-values and shtuka functions for Drinfeld modules on elliptic curves

We make a detailed account of sign-normalized rank 1 Drinfeld $$\mathbf {A}$$ A -modules, for $$\mathbf {A}$$ A the coordinate ring of an elliptic curve over a finite field, in order to provide a parallel theory to the Carlitz module for $$\mathbb {F}_q[t]$$ F q [ t ] . Using precise formulas for the shtuka function for $$\mathbf {A}$$ A , we obtain a product formula for the fundamental period of the Drinfeld module. Using the shtuka function we find identities for deformations of reciprocal sums and as a result prove special value formulas for Pellarin L-series in terms of an Anderson–Thakur function. We also give a new proof of a log-algebraicity theorem of Anderson.
Complex interaction processes we need to visualize that successfully fill the quantum cup of a detector

Sensors are measuring tools. In any measurement, we have at least two different kinds of interactants. We never know all there are to know about any one of these interactants and the interaction processes that are mostly invisible. Yet, our engineering innovation driven evolution is persisting for over five million years. It is then important to articulate explicitly our Interaction Process Mapping Thinking (IPM-T) that we keep applying in the real world without formally recognizing it. We present how the systematic application of IPM-T removes century old wave-particle duality by introducing a model of hybrid photon. It seamlessly bridges the quantum and the classical worlds. Photons are discrete energy packets only at the moment of emission; then they evolve diffractively and propagate as classical waves. Thus, "interference of a single indivisible photon" is only a non-causal assertion. We apply IPM-T to improve the photoelectric equation and we obtain Non-Interaction of Wave (NIW) amplitudes. Note that Huygens explicitly articulated NIW by postulating his "secondary spherical wavelets." Later Fresnel incorporated this postulate in the now famous Huygens- Fresnel (HF) diffraction integral. Most modern optical science and engineering are based upon propagating EM waves through optical devices and systems using this integral in some form or other. Maxwell’s wave equation accepts HF integral as its solution. Systematic application of IPM-T to our causal and working mathematical equations, along with NIW in interferometric experiments, reveal that Superposition Effects can emerge only when the interacting material dipoles respond, whether classically or quantum mechanically, to the joint stimulations due to all the simultaneously superposed waves. This indicates the non-causality of our belief that a single indivisible photon can interfere by itself. We would not have a causally evolving universe had any stable elementary particle were to change itself through selfinterference. Further, our working superposition equations always contain two or more terms representing two or more independently evolving entities. That is why we need physical instruments with two or more independent channels of propagation along with appropriately placed detectors to generate physical superposition effects. Nature does not violate causality. Otherwise, our causally framed equations would not have been working so elegantly.
Large deviation principle for stochastic integrals and stochastic differential equations driven by infinite-dimensional semimartingales

Abstract The paper concerns itself with establishing large deviation principles for a sequence of stochastic integrals and stochastic differential equations driven by general semimartingales in infinite-dimensional settings. The class of semimartingales considered is broad enough to cover Banach space-valued semimartingales and the martingale random measures. Simple usable expressions for the associated rate functions are given in this abstract setup. As illustrated through several concrete examples, the results presented here provide a new systematic approach to the study of large deviation principles for a sequence of Markov processes.
Lifts of pseudo-Anosov homeomorphisms of nonorientable surfaces have vanishing SAF invariant

We show that any pseudo-Anosov map that is a lift of pseudo-Anosov homeomorphism of a nonorientable surface has vanishing SAF invariant. We also provide a criterion to certify that a pseudo-Anosov map is not such a lift.
A Tight Bound on the Projective Dimension of Four Quadrics

Abstract Motivated by Stillman's question, we show that the projective dimension of an ideal generated by four quadric forms in a polynomial ring is at most 6; moreover, this bound is tight. We achieve this bound, in part, by giving a characterization of the low degree generators of ideals primary to height three primes of multiplicities one and two.
Image Acquisition System Using On Sensor Compressed Sampling Technique

Advances in CMOS technology have made high resolution image sensors possible. These image sensor pose significant challenges in terms of the amount of raw data generated, energy efficiency and frame rate. This paper presents a new design methodology for an imaging system and a simplified novel image sensor pixel design to be used in such system so that Compressed Sensing (CS) technique can be implemented easily at the sensor level. This results in significant energy savings as it not only cuts the raw data rate but also reduces transistor count per pixel, decreases pixel size, increases fill factor, simplifies ADC, JPEG encoder and JPEG decoder design and decreases wiring as well as address decoder size by half. Thus CS has the potential to increase the resolution of image sensors for a given technology and die size while significantly decreasing the power consumption and design complexity. We show that it has potential to reduce power consumption by about 23%-65%.
Signal-plus-noise matrix models: eigenvector deviations and fluctuations

Summary Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science and applied mathematics. In this paper we characterize the behaviour of perturbed eigenvectors for a range of signal-plus-noise matrix models encountered instatistical and random-matrix-theoretic settings. We establish both first-order approximation results, i.e., sharp deviations, and second-order distributional limit theory, i.e., fluctuations. The concise methodology presented in this paper synthesizes tools rooted in two core concepts, namely deterministic decompositions of matrix perturbations and probabilistic matrix concentration phenomena. We illustrate our theoretical results with simulation examples involving stochastic block model random graphs.
Fast Threshold Tests for Detecting Discrimination

Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative of discrimination. This technique, however, requires fitting a complex Bayesian latent variable model for which inference is often computationally challenging. Here we develop a method for fitting threshold tests that is two orders of magnitude faster than the existing approach, reducing computation from hours to minutes. To achieve these performance gains, we introduce and analyze a flexible family of probability distributions on the interval [0, 1] -- which we call discriminant distributions -- that is computationally efficient to work with. We demonstrate our technique by analyzing 2.7 million police stops of pedestrians in New York City.
Lower bounds for approximating the matching polytope

We prove that any linear program that approximates the matching polytope on n-vertex graphs up to a factor of (1 + e) for any [EQUATION] must have at least [EQUATION] inequalities where 0 α e) approximating linear program obtained by dropping the odd set constraints of size larger than (1 + e)/e from the description of the matching polytope. Previously, a tight lower bound of 2Ω(n) was only known for [EQUATION] [22, 5] whereas for [EQUATION], the best lower bound was 2Ω(1/e) [22]. The key new ingredient in our proof is a close connection to the non-negative rank of a lopsided version of the unique disjointness matrix.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding.

For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time. Recent works seek to replace the 3D convolution layer with convolution blocks, e.g. structured combinations of 2D and 1D convolution layers. In this paper, we propose a novel convolution block, Parallel Separable 3D Convolution (PmSCn), which applies m parallel streams of n 2D and one 1D convolution layers along different dimensions. We first mathematically justify the need of parallel streams (Pm) to replace a single 3D convolution layer through tensor decomposition. Then we jointly replace consecutive 3D convolution layers, common in modern network architectures, with the multiple 2D convolution layers (Cn). Lastly, we empirically show that PmSCn is applicable to different backbone architectures, such as ResNet, DenseNet, and UNet, for different applications, such as video action recognition, MRI brain segmentation, and electron microscopy segmentation. In all three applications, we replace the 3D convolution layers in state-of-the art models with PmSCn and achieve around 14% improvement in test performance and 40% reduction in model size and on average.
Distribution-independent Reliable Learning

We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than other types. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most , (ii) its false negative error rate is at most more than that of the best positive reliable classifier from the class. A closely related notion is fully reliable agnostic learning, which considers partial classifiers that are allowed to predict “unknown” on some inputs. The best fully reliable partial classifier is one that makes no errors and minimizes the probability of predicting “unknown”, and the goal in fully reliable learning is to output a hypothesis that is almost as good as the best fully reliable partial classifier from a class. For distribution-independent learning, the best known algorithms for PAC learning typically utilize polynomial threshold representations, while the state of the art agnostic learning algorithms use point-wise polynomial approximations. We show that one-sided polynomial approximations, an intermediate notion between polynomial threshold representations and point-wise polynomial approximations, suffice for learning in the reliable agnostic settings. We then show that majorities can be fully reliably learned and disjunctions of majorities can be positive reliably learned, through constructions of appropriate one-sided polynomial approximations. Our fully reliable algorithm for majorities provides the first evidence that fully reliable learning may be strictly easier than agnostic learning. Our algorithms also satisfy strong attribute-efficiency properties, and in many cases they provide smooth tradeoffs between sample complexity and running time.
Approximating Dynamic Time Warping and Edit Distance for a Pair of Point Sequences

We present the first subquadratic algorithms for computing similarity between a pair of point sequences in R^d, for any fixed d > 1, using dynamic time warping (DTW) and edit distance, assuming that the point sequences are drawn from certain natural families of curves. In particular, our algorithms compute (1 + eps)-approximations of DTW and ED in near-linear time for point sequences drawn from k-packed or k-bounded curves, and subquadratic time for backbone sequences. Roughly speaking, a curve is k-packed if the length of its intersection with any ball of radius r is at most kr, and it is k-bounded if the sub-curve between two curve points does not go too far from the two points compared to the distance between the two points. In backbone sequences, consecutive points are spaced at approximately equal distances apart, and no two points lie very close together. Recent results suggest that a subquadratic algorithm for DTW or ED is unlikely for an arbitrary pair of point sequences even for d = 1. The commonly used dynamic programming algorithms for these distance measures reduce the problem to computing a minimum-weight path in a grid graph. Our algorithms work by constructing a small set of rectangular regions that cover the grid vertices. The weights of vertices inside each rectangle are roughly the same, and we develop efficient procedures to compute the approximate minimum-weight paths through these rectangles.
Predicting the Generalization Gap in Deep Networks with Margin Distributions

As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should be predicted from the training data and network parameters. In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap. Our measure is based on the concept of margin distribution, which are the distances of training points to the decision boundary. We find that it is necessary to use margin distributions at multiple layers of a deep network. On the CIFAR-10 and the CIFAR-100 datasets, our proposed measure correlates very strongly with the generalization gap. In addition, we find the following other factors to be of importance: normalizing margin values for scale independence, using characterizations of margin distribution rather than just the margin (closest distance to decision boundary), and working in log space instead of linear space (effectively using a product of margins rather than a sum). Our measure can be easily applied to feedforward deep networks with any architecture and may point towards new training loss functions that could enable better generalization.
A Two-Stage Auction Mechanism for Cloud Resource Allocation

The contemporary literature on cloud resource allocation is mostly focused on studying the interactions between customers and cloud managers. Nevertheless, the recent growth in the customers’ demands and the emergence of private cloud providers (CPs) entice the cloud managers to rent extra resources from the CPs so as to handle their backlogged tasks and attract more customers. This also renders the interactions between the cloud managers and the CPs an important problem to study. In this paper, we investigate both interactions through a two-stage auction mechanism. For the interactions between customers and cloud managers, we adopt the options-based sequential auctions (OBSAs) to design the cloud resource allocation paradigm. As compared to existing works, our framework can handle customers with heterogeneous demands, provide truthfulness as the dominant strategy, enjoy a simple winner determination procedure, and preclude the delayed entrance issue. We also provide the performance analysis of the OBSAs, which is among the first in literature. Regarding the interactions between cloud managers and CPs, we propose two parallel markets for resource gathering, and capture the selfishness of the CPs by their offered prices . We conduct a comprehensive analysis of the two markets and identify the bidding strategies of the cloud managers.
The Case for RodentStore, an Adaptive, Declarative Storage System

Recent excitement in the database community surrounding new applications—analytic, scientific, graph, geospatial, etc.—has led to an explosion in research on database storage systems. New storage systems are vital to the database community, as they are at the heart of making database systems perform well in new application domains. Unfortunately, each such system also represents a substantial engineering effort including a great deal of duplication of mechanisms for features such as transactions and caching. In this paper, we make the case for RodentStore, an adaptive and declarative storage system providing a high-level interface for describing the physical representation of data. Specifically, RodentStore uses a declarative storage algebra whereby administrators (or database design tools) specify how a logical schema should be grouped into collections of rows, columns, and/or arrays, and the order in which those groups should be laid out on disk. We describe the key operators and types of our algebra, outline the general architecture of RodentStore, which interprets algebraic expressions to generate a physical representation of the data, and describe the interface between RodentStore and other parts of a database system, such as the query optimizer and executor. We provide a case study of the potential use of RodentStore in representing dense geospatial data collected from a mobile sensor network, showing the ease with which different storage layouts can be expressed using some of our algebraic constructs and the potential performance gains that a RodentStore-built storage system can offer.
Data-driven Random Fourier Features using Stein Effect

Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC) integration [Yang et al., 2014]. A limitation of the current approaches is that all the features receive an equal weight summing to 1. In this paper, we propose a novel shrinkage estimator from "Stein effect", which provides a data-driven weighting strategy for random features and enjoys theoretical justifications in terms of lowering the empirical risk. We further present an efficient randomized algorithm for large-scale applications of the proposed method. Our empirical results on six benchmark data sets demonstrate the advantageous performance of this approach over representative baselines in both kernel approximation and supervised learning tasks.
Residualized Factor Adaptation for Community Social Media Prediction Tasks

Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for incorporating socio-demographic contexts.
Closed symmetric 3-differentials on complex surfaces

We give a necessary and sufficient condition for a non-degenerate symmetric 3-differential with non-zero Blaschke curvature on a complex surface to be locally representable as a product of three closed holomorphic 1-forms. We give two versions of this condition corresponding to different choices of coordinates, one of which defines a coordinate-free differential operator, answering a question of Bogomolov and de Oliveira.
Moving-centre monotonicity formulae for minimal submanifolds and related equations

Abstract Monotonicity formulae play a crucial role for many geometric PDEs, especially for their regularity theories. For minimal submanifolds in a Euclidean ball, the classical monotonicity formula implies that if such a submanifold passes through the centre of the ball, then its area is at least that of the equatorial disk. Recently Brendle and Hung proved a sharp area bound for minimal submanifolds when the prescribed point is not the centre of the ball, which resolved a conjecture of Alexander, Hoffman and Osserman. Their proof involves asymptotic analysis of an ingeniously chosen vector field, and the divergence theorem. In this article we prove a sharp ‘moving-centre’ monotonicity formula for minimal submanifolds, which implies the aforementioned area bound. We also describe similar moving-centre monotonicity formulae for stationary p-harmonic maps, mean curvature flow and the harmonic map heat flow.
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.
Sample-Based High-Dimensional Convexity Testing.

In the problem of high-dimensional convexity testing, there is an unknown set S in the n-dimensional Euclidean space which is promised to be either convex or c-far from every convex body with respect to the standard multivariate normal distribution. The job of a testing algorithm is then to distinguish between these two cases while making as few inspections of the set S as possible. In this work we consider sample-based testing algorithms, in which the testing algorithm only has access to labeled samples (x,S(x)) where each x is independently drawn from the normal distribution. We give nearly matching sample complexity upper and lower bounds for both one-sided and two-sided convexity testing algorithms in this framework. For constant c, our results show that the sample complexity of one-sided convexity testing is exponential in n, while for two-sided convexity testing it is exponential in the square root of n.
Pseudorandom generators for width-3 branching programs

We construct pseudorandom generators of seed length O(log(n)· log(1/є)) that є-fool ordered read-once branching programs (ROBPs) of width 3 and length n. For unordered ROBPs, we construct pseudorandom generators with seed length O(log(n) · poly(1/є)). This is the first improvement for pseudorandom generators fooling width 3 ROBPs since the work of Nisan [Combinatorica, 1992]. Our constructions are based on the “iterated milder restrictions” approach of Gopalan et al. [FOCS, 2012] (which further extends the Ajtai-Wigderson framework [FOCS, 1985]), combined with the INW-generator [STOC, 1994] at the last step (as analyzed by Braverman et al. [SICOMP, 2014]). For the unordered case, we combine iterated milder restrictions with the generator of Chattopadhyay et al. [CCC, 2018]. Two conceptual ideas that play an important role in our analysis are: (1) A relabeling technique allowing us to analyze a relabeled version of the given branching program, which turns out to be much easier. (2) Treating the number of colliding layers in a branching program as a progress measure and showing that it reduces significantly under pseudorandom restrictions. In addition, we achieve nearly optimal seed-length O(log(n/є)) for the classes of: (1) read-once polynomials on n variables, (2) locally-monotone ROBPs of length n and width 3 (generalizing read-once CNFs and DNFs), and (3) constant-width ROBPs of length n having a layer of width 2 in every consecutive polylog(n) layers.
Single-Image Depth Perception in the Wild

This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset “Depth in the Wild” consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.
Quantitative (p,q) theorems in combinatorial geometry

Abstract We show quantitative versions of classical results in discrete geometry, where the size of a convex set is determined by some non-negative function. We give versions of this kind for the selection theorem of Barany, the existence of weak epsilon-nets for convex sets and the ( p , q ) theorem of Alon and Kleitman. These methods can be applied to functions such as the volume, surface area or number of points of a discrete set. We also give general quantitative versions of the colorful Helly theorem for continuous functions.
Towards Complex Artificial Life

An object-oriented combinator chemistry was used to construct an artificial organism with a system architecture possessing characteristics necessary for organisms to evolve into more complex forms....
Geometry and rigidity of mapping class groups

We study the large scale geometry of mapping class groups MCG(S), using hyperbolicity properties of curve complexes. We show that any self quasi-isometry of MCG(S) (outside a few sporadic cases) is a bounded distance away from a left-multiplication, and as a conse- quence obtain quasi-isometric rigidity for MCG(S), namely that groups quasi-isometric to MCG(S) are equivalent to it up to extraction of finite- index subgroups and quotients with finite kernel. (The latter theorem was proved by Hamenstadt using different methods). As part of our approach we obtain several other structural results: a description of the tree-graded structure on the asymptotic cone of MCG(S); a characterization of the image of the curve complex projections map from MCG(S) to Q YS C(Y ); and a construction of �-hulls in MCG(S), an analogue of convex hulls.