cssi/SciSciGPT-SciSciNet huggingface dataset:
Recovery time of matter Airy beams using the path integral quantum trajectory model

Abstract Following their discovery in the late 1970s, optical Airy beams have found numerous applications in technologies such as microscopy and optical trapping, many of which are based on the wave packets’ unique features such as zero or minimal diffraction, self-acceleration, and self-healing. Recent advancements have shown that Airy beams can also be produced using matter waves with many of the same unique characteristics of their optical counterparts. We present here a study of the recovery time of damaged matter Airy wave packets in free space and a nonlinear Kerr-type medium. We show that in free space the recovery time increases approximately linearly with mass and is independent of other kinematical parameters such as momentum, velocity, and spatial width. In the Kerr-type medium, recovery time is decreased compared to free space and does not scale linearly with mass. In order to study matter Airy beams, we introduce the Path Integral Quantum Trajectory model as a new computational tool for the study of non-relativistic, quantum mechanical wave packets and demonstrate its effectiveness in dealing with heavy particle dynamics.
The structure of the Kauffman bracket skein algebra at roots of unity

This paper examines the structure of the Kauffman bracket skein algebra of a punctured surface at roots of unity. A criterion that determines when a collection of skeins forms a basis of the skein algebra as a module over the $$SL(2,{{\mathbb {C}}})$$ characters of the fundamental group of the surface, with appropriate localization is given. This is used to prove that when the algebra is localized so that every nonzero element of the center has a multiplicative inverse, it is a division algebra. Finally, it is proved that the localized skein algebra can be split as a module over its center as a tensor product of two commutative subalgebras.
Quickest Detection for Changes in Maximal kNN Coherence of Random Matrices

This paper addresses the problem of quickest detection of a change in the maximal coherence between columns of an $n\times p$ random matrix based on a sequence of matrix observations having a single unknown change point. The random matrix is assumed to have identically distributed rows and the maximal coherence is defined as the largest of the $p \atopwithdelims ()2$ correlation coefficients associated with any row. Likewise the $k$ nearest neighbor (kNN) coherence is defined as the $k$ -th largest of these correlation coefficients. The forms of the pre- and postchange distributions of the observed matrices are assumed to belong to the family of elliptically contoured densities with sparse dispersion matrices but are otherwise unknown. A nonparametric stopping rule is proposed that is based on the maximal kNN sample coherence between columns of each observed random matrix. This is a summary statistic that is related to a test of existence of a hub vertex in a sample correlation graph having degree at least $k$ . Performance bounds on the delay and false alarm performance of the proposed stopping rule are obtained in the purely high-dimensional regime where $p\rightarrow \infty$ and $n$ is fixed. When the prechange dispersion matrix is diagonal it is shown that, among all functions of the proposed summary statistic, the proposed stopping rule is asymptotically optimal under a minimax quickest change detection model as the stopping threshold approaches infinity. The theory developed also applies to sequential hypothesis testing and fixed sample size tests.
Adaptive neural networks for efficient inference

We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example. By allowing examples correctly classified using early layers of the system to exit, we avoid the computational time associated with full evaluation of the network. We extend this to learn a network selection system that adaptively selects the network to be evaluated for each example. We show that computational time can be dramatically reduced by exploiting the fact that many examples can be correctly classified using relatively efficient networks and that complex, computationally costly networks are only necessary for a small fraction of examples. We pose a global objective for learning an adaptive early exit or network selection policy and solve it by reducing the policy learning problem to a layer-by-layer weighted binary classification problem. Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the ImageNet image recognition challenge with minimal (< 1%) loss of top5 accuracy.
The Case for a Structured Approach to Managing Unstructured Data.

The challenge of managing unstructured data represents perhaps the largest data management opportunity for our community since managing relational data. And yet we are risking letting this opportunity go by, ceding the playing field to other players, ranging from communities such as AI, KDD, IR, Web, and Semantic Web, to industrial players such as Google, Yahoo, and Microsoft. In this essay we explore what we can do to improve upon this situation. Drawing on the lessons learned while managing relational data, we outline a structured approach to managing unstructured data. We conclude by discussing the potential implications of this approach to managing other kinds of non-relational data, and to the identify of our field.
Motif-based analysis of power grid robustness under attacks

Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topo-logical measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study of motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.
Cyclically symmetric lozenge tilings of a hexagon with four holes

Abstract The work of Mills, Robbins, and Rumsey on cyclically symmetric plane partitions yields a simple product formula for the number of lozenge tilings of a regular hexagon, which are invariant under rotation by 120°. In this paper we generalize this result by enumerating the cyclically symmetric lozenge tilings of a hexagon in which four triangles have been removed in the center.
A nondiagrammatic description of the Connes–Kreimer Hopf algebra

Abstract We demonstrate that the fundamental algebraic structure underlying the Connes–Kreimer Hopf algebra–the insertion pre-Lie structure on graphs–corresponds directly to the canonical pre-Lie structure of polynomial vector fields. Using this fact, we construct a Hopf algebra built from tensors that is isomorphic to a version of the Connes–Kreimer Hopf algebra that first appeared in the perturbative renormalization of quantum field theories.
Distributed online Big Data classification using context information

Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop a distributed online learning algorithm for which we can prove sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithm.
Estimates on the modulus of expansion for vector fields solving nonlinear equations

Abstract In this article, by extending the method of Andrews and Clutterbuck (2011) [2] we prove a sharp estimate on the expansion modulus of the gradient of the logarithm of the parabolic kernel to the Schrodinger operator with convex potential on a bounded convex domain. The result improves an earlier work of Brascamp–Lieb which asserts the log-concavity of the parabolic kernel. We also give an alternate proof to a corresponding estimate on the first eigenfunction of the Schrodinger operator, obtained firstly by Andrews and Clutterbuck via the study of the asymptotics to a parabolic problem. Our proof is more direct via an elliptic maximum principle. An alternate proof of the fundamental gap theorem of Andrews and Clutterbuck (2011) [2] , by considering the quotient of moduli of continuity, is also obtained. Moreover we derive a Neumann eigenvalue comparison result and some other lower estimates on the first Neumann eigenvalue for Laplace operator with a drifting term, including an explicit estimate on a conjecture of P. Li.
Relational foundations for functorial data migration

We study the data transformation capabilities associated with schemas that are presented by directed multi-graphs and path equations. Unlike most approaches which treat graph-based schemas as abbreviations for relational schemas, we treat graph-based schemas as categories. A schema S is a finitely-presented category, and the collection of all S-instances forms a category, S-inst. A functor F between schemas S and T, which can be generated from a visual mapping between graphs, induces three adjoint data migration functors, Sigma_F : S-inst -> T-inst, Pi_F : S-inst -> T-inst, and Delta_F : T-inst -> S-inst. We present an algebraic query language FQL based on these functors, prove that FQL is closed under composition, prove that FQL can be implemented with the select-project-product-union relational algebra (SPCU) extended with a key-generation operation, and prove that SPCU can be implemented with FQL.
Modeling and Analysis of Non-Unique Behaviors in Multiple Frictional Impacts.

Many fundamental challenges in robotics, based in manipulation or locomotion, require making and breaking contact with the environment. To represent the complexity of frictional contact events, impulsive impact models are especially popular, as they often lead to mathematically and computationally tractable approaches. However, when two or more impacts occur simultaneously, the precise sequencing of impact forces is generally unknown, leading to the potential for multiple possible outcomes. This simultaneity is far from pathological, and occurs in many common robotics applications. In this work, we propose an approach for resolving simultaneous frictional impacts, represented as a differential inclusion. Solutions to our model, an extension to multiple contacts of Routh's method, naturally capture the set of potential post-impact velocities.We prove that solutions to the presented model must terminate. This is, to the best of our knowledge, the first such guarantee for set-valued outcomes to simultaneous frictional impacts.
{IODINE}: Verifying Constant-Time Execution of Hardware

To be secure, cryptographic algorithms crucially rely on the underlying hardware to avoid inadvertent leakage of secrets through timing side channels. Unfortunately, such timing channels are ubiquitous in modern hardware, due to its labyrinthine fast-paths and optimizations. A promising way to avoid timing vulnerabilities is to devise – and verify – conditions under which a hardware design is free of timing variability, i.e., executes in constant-time. In this paper, we present IODINE: a clock-precise, constant-time approach to eliminating timing side channels in hardware. IODINE succeeds in verifying various open source hardware designs in seconds and with little developer effort. IODINE also discovered two constant-time violations: one in a floating-point unit and another one in an RSA encryption module.
TET-Catalyzed 5-Carboxylcytosine Promotes CTCF Binding to Suboptimal Sequences Genome-wide

Summary The mechanisms supporting dynamic regulation of CTCF-binding sites remain poorly understood. Here we describe the TET-catalyzed 5-methylcytosine derivative, 5-carboxylcytosine (5caC), as a factor driving new CTCF binding within genomic DNA. Through a combination of in vivo and in vitro approaches, we reveal that 5caC generally strengthens CTCF association with DNA and facilitates binding to suboptimal sequences. Dramatically, profiling of CTCF binding in a cellular model that accumulates genomic 5caC identified ~13,000 new CTCF sites. The new sites were enriched for overlapping 5caC and were marked by an overall reduction in CTCF motif strength. As CTCF has multiple roles in gene expression, these findings have wide-reaching implications and point to induced 5caC as a potential mechanism to achieve differential CTCF binding in cells.
Neural packet classification

Packet classification is a fundamental problem in computer networking. This problem exposes a hard tradeoff between the computation and state complexity, which makes it particularly challenging. To navigate this tradeoff, existing solutions rely on complex hand-tuned heuristics, which are brittle and hard to optimize. In this paper, we propose a deep reinforcement learning (RL) approach to solve the packet classification problem. There are several characteristics that make this problem a good fit for Deep RL. First, many existing solutions iteratively build a decision tree by splitting nodes in the tree. Second, the effects of these actions (e.g., splitting nodes) can only be evaluated once the entire tree is built. These two characteristics are naturally captured by the ability of RL to take actions that have sparse and delayed rewards. Third, it is computationally efficient to generate data traces and evaluate decision trees, which alleviate the notoriously high sample complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective. It produces compact decision trees optimized for a specific set of rules and a given performance metric, such as classification time, memory footprint, or a combination of the two. Evaluation on Class-Bench shows that NeuroCuts outperforms existing hand-crafted algorithms in classification time by 18% at the median, and reduces both classification time and memory footprint by up to 3X.
Towards Automatic Concept-based Explanations

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explanation methods provide explanations through feature importance scores, which identify features that are important for each individual input. However, how to systematically summarize and interpret such per sample feature importance scores itself is challenging. In this work, we propose principles and desiderata for \emph{concept} based explanation, which goes beyond per-sample features to identify higher level human-understandable concepts that apply across the entire dataset. We develop a new algorithm, ACE, to automatically extract visual concepts. Our systematic experiments demonstrate that \alg discovers concepts that are human-meaningful, coherent and important for the neural network's predictions.
A Graph Theoretic Additive Approximation of Optimal Transport

Transportation cost is an attractive similarity measure between probability distributions due to its many useful theoretical properties. However, solving optimal transport exactly can be prohibitively expensive. Therefore, there has been significant effort towards the design of scalable approximation algorithms. Previous combinatorial results [Sharathkumar, Agarwal STOC '12, Agarwal, Sharathkumar STOC '14] have focused primarily on the design of near-linear time multiplicative approximation algorithms. There has also been an effort to design approximate solutions with additive errors [Cuturi NIPS '13, Altschuler \etal\ NIPS '17, Dvurechensky \etal\, ICML '18, Quanrud, SOSA '19] within a time bound that is linear in the size of the cost matrix and polynomial in $C/\delta$; here $C$ is the largest value in the cost matrix and $\delta$ is the additive error. We present an adaptation of the classical graph algorithm of Gabow and Tarjan and provide a novel analysis of this algorithm that bounds its execution time by $\BigO(\frac{n^2 C}{\delta}+ \frac{nC^2}{\delta^2})$. Our algorithm is extremely simple and executes, for an arbitrarily small constant $\eps$, only $\lfloor \frac{2C}{(1-\eps)\delta}\rfloor + 1$ iterations, where each iteration consists only of a Dijkstra-type search followed by a depth-first search. We also provide empirical results that suggest our algorithm is competitive with respect to a sequential implementation of the Sinkhorn algorithm in execution time. Moreover, our algorithm quickly computes a solution for very small values of $\delta$ whereas Sinkhorn algorithm slows down due to numerical instability.
Do truly unidirectional surface plasmon-polaritons exist?

In this work, we revisit the topic of surface waves on nonreciprocal plasmonic structures, and clarify whether strictly unidirectional surface plasmon-polaritons are allowed to exist in this material platform. By investigating different three-dimensional configurations and frequency regimes, we theoretically show that, while conventional surface magneto-plasmons are not strictly unidirectional due to nonlocal effects, consistent with recent predictions made in the literature, another important class of one-way surface plasmon-polaritons, existing at an interface with an opaque isotropic material, robustly preserves their unidirectionality even in the presence of nonlocality, and for arbitrarily small levels of dissipation. We also investigate the extreme behavior of terminated unidirectional waveguiding structures, for both classes of surface waves, and discuss their counterintuitive implications.
Vanishing of degree 3 cohomological invariants

Abstract For a complex algebraic variety X, we show that triviality of the degree three unramified cohomology H 0 ( X , H 3 ) (occurring on the second page of the Bloch-Ogus spectral sequence [1] ) follows from a condition on the integral Chow group C H 2 X and the integral cohomology group H 3 ( X , Z ) . In the case that X is an appropriate approximation to the classifying stack BG of a finite p-group G, this result states that the group G has no degree three cohomological invariants. As a corollary we show that the nonabelian groups of order p 3 for odd prime p have no degree three cohomological invariants.
Intended and unintended consequences of US renewable energy policies

Abstract Over the past four decades, the US has employed a range of policies to move the US energy mix towards more renewable and domestic resources. The purpose of this paper is to assess the employed policies using the following criteria: 1) Did the policy achieve the objective of increasing production and consumption of the targeted renewable energy? 2) Did the policy reduce US dependence on foreign energy? 3) What was the cost of the renewable energy increase, and how does it compare with the government estimated Social Cost of Carbon (SCC) around $40/ton ($0.04/kg) or with the implicit SSC often associated with achieving the aims of the Paris accord of $160/ton ($0.16/kg) or higher? 4) What were the unintended consequences of the policy? The important conclusions are: 1) Policy makers prefer regulation to pricing mechanisms; 2) The success in achieving quantitative targets is mixed; 3) The social cost of achieving the renewable penetration is generally higher than the SCC. With the renewable energy growth required to achieve the levels needed to achieve the Paris accord, the unintended consequence of costs rise significantly with scale. Most of the analysis done to date has only examined impacts and costs at low levels of penetration.
Speed and Separation Monitoring using On-Robot Time-of-Flight Laser-ranging Sensor Arrays

In this paper, a speed and separation monitoring (SSM) based safety controller using three time-of-flight ranging sensor arrays fastened to the robot links, is implemented. Based on the human-robot minimum distance and their relative velocities, a controller output characterized by a modulating robot operation speed is obtained. To avert self-avoidance, a self occlusion detection method is implemented using ray-casting technique to filter out the distance values associated with the robot-self and the restricted robot workspace. For validation, the robot workspace is monitored using a motion capture setup to create a digital twin of the human and robot. This setup is used to compare the safety, performance and productivity of various versions of SSM safety configurations based on minimum distance between human and robot calculated using on-robot Time-of-Flight sensors, motion capture and a 2D scanning lidar.
Rapid Phenotypic and Metabolomic Domestication of Wild Penicillium Molds on Cheese.

ABSTRACT Fermented foods provide novel ecological opportunities for natural populations of microbes to evolve through successive recolonization of resource-rich substrates. Comparative genomic data have reconstructed the evolutionary histories of microbes adapted to food environments, but experimental studies directly demonstrating the process of domestication are lacking for most fermented food microbes. Here, we show that during adaptation to cheese, phenotypic and metabolomic traits of wild Penicillium molds rapidly change to produce domesticated phenotypes with properties similar to those of the industrial cultures used to make Camembert and other bloomy rind cheeses. Over a period of just a few weeks, populations of wild Penicillium strains serially passaged on cheese had reduced pigment, spore, and mycotoxin production. Domesticated strains also had a striking change in volatile metabolite production, shifting from production of earthy or musty volatile compounds (e.g., geosmin) to fatty and cheesy volatiles (e.g., 2-nonanone, 2-undecanone). RNA sequencing demonstrated a significant decrease in expression of 356 genes in domesticated strains, with an enrichment of many secondary metabolite production pathways in these downregulated genes. By manipulating the presence of neighboring microbial species and overall resource availability, we demonstrate that the limited competition and high nutrient availability of the cheese environment promote rapid trait evolution of Penicillium molds. IMPORTANCE Industrial cultures of filamentous fungi are used to add unique aesthetics and flavors to cheeses and other microbial foods. How these microbes adapted to live in food environments is generally unknown as most microbial domestication is unintentional. Our work demonstrates that wild molds closely related to the starter culture Penicillium camemberti can readily lose traits and quickly shift toward producing desirable aroma compounds. In addition to experimentally demonstrating a putative domestication pathway for P. camemberti, our work suggests that wild Penicillium isolates could be rapidly domesticated to produce new flavors and aesthetics in fermented foods.
A new basis for the representation ring of a Weyl group

Let W be a Weyl group. We define a new basis for the Grothendieck group of representations of W. This basis contains on the one hand the special representations of W and on the other hand the representations carried by the left cells of W. We show that the representations in the new basis have a certain bipositivity property.
Optimizing data-intensive computations in existing libraries with split annotations

Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate representations (IRs) that apply optimizations such as loop fusion under existing high-level APIs such as NumPy and TensorFlow. Even though these techniques generally do not require changes to user applications, they require intrusive changes to the library itself: often, library developers must rewrite each function using a new IR. In this paper, we propose a new technique called split annotations (SAs) that enables key data movement optimizations over unmodified library functions. SAs only require developers to annotate functions and implement an API that specifies how to partition data in the library. The annotation and API describe how to enable cross-function data pipelining and parallelization, while respecting each function's correctness constraints. We implement a parallel runtime for SAs in a system called Mozart. We show that Mozart can accelerate workloads in libraries such as Intel MKL and Pandas by up to 15x, with no library modifications. Mozart also provides performance gains competitive with solutions that require rewriting libraries, and can sometimes outperform these systems by up to 2x by leveraging existing hand-optimized code.
Cortical Foxp2 Supports Behavioral Flexibility and Developmental Dopamine D1 Receptor Expression

Genetic studies have associated FOXP2 variation with speech and language disorders and other neurodevelopmental disorders (NDDs) involving pathology of the cortex. In this brain region, FoxP2 is expressed from development into adulthood, but little is known about its downstream molecular and behavioral functions. Here, we characterized cortex-specific Foxp2 conditional knockout mice and found a major deficit in reversal learning, a form of behavioral flexibility. In contrast, they showed normal activity levels, anxiety, and vocalizations, save for a slight decrease in neonatal call loudness. These behavioral phenotypes were accompanied by decreased cortical dopamine D1 receptor (D1R) expression at neonatal and adult stages, while general cortical development remained unaffected. Finally, using single-cell transcriptomics, we identified at least five excitatory and three inhibitory D1R-expressing cell types in neonatal frontal cortex, and we found changes in D1R cell type composition and gene expression upon cortical Foxp2 deletion. Strikingly, these alterations included non-cell-autonomous changes in upper layer neurons and interneurons. Together, these data support a role for Foxp2 in the development of dopamine-modulated cortical circuits and behaviors relevant to NDDs.
Spectrum of weighted composition operators part V spectrum and essential spectra of weighted rotation-like operators

We introduce the class of weighted “rotation-like” operators and study the general properties of the essential spectra of such operators. We then use this approach to investigate, and in some cases completely describe, the essential spectra of weighted rotation operators in Banach spaces of measurable and analytic functions.
nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data

In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frameworks such as TensorFlow and PyTorch with minimal code changes. However, the class of supported models was limited to relatively shallow networks with polynomial activations. Here, we introduce nGraph-HE2, which extends nGraph-HE to enable privacy-preserving inference on standard, pre-trained models using their native activation functions and number fields (typically real numbers). The proposed framework leverages the CKKS scheme, whose support for real numbers is friendly to data science, and a client-aided model using a two-party approach to compute activation functions. We first present CKKS-specific optimizations, enabling a 3x-88x runtime speedup for scalar encoding, and doubling the throughput through a novel use of CKKS plaintext packing into complex numbers. Second, we optimize ciphertext-plaintext addition and multiplication, yielding 2.6x-4.2x runtime speedup. Third, we exploit two graph-level optimizations: lazy-rescaling and depth-aware encoding, which allow us to significantly improve performance. Together, these optimizations enable state-of-the-art throughput of 1,998 images/s on the CryptoNets network. Using the client-aided model, we also present homomorphic evaluation of (to our knowledge) the largest network to date, namely, pre-trained MobileNetV2 models on the ImageNet dataset, with 60.4%/82.7% top-1/top-5 accuracy and an amortized runtime of 381 ms/image.
On Post-Processing the Results of Quantum Optimizers

The use of quantum computing for applications involving optimization has been regarded as one of the areas it may prove to be advantageous (against classical computation). To further improve the solutions, post-processing techniques are often used on the results of quantum optimization. One such recent approach is the Multi Qubit Correction (MQC) algorithm by Dorband. In this paper, we will discuss and analyze the strengths and weaknesses of this technique. Based on our discussion, we perform an experiment on (i) how pairing heuristics on the input of MQC can affect the results of a quantum optimizer and (ii) a comparison between MQC and the built-in optimization method that D-wave Systems offers. Among our results, we are able to show that the built-in post-processing rarely beats MQC in our tests. We hope that by using the ideas and insights presented in this paper, researchers and developers will be able to make a more informed decision on what kind of post-processing methods to use for their quantum optimization needs.
Quality of maternity care provided by private sector healthcare facilities in three states of India: a situational analysis

Better quality of care around the time of childbirth can significantly improve maternal and newborn survival. In countries like India, where the private sector contributes to a considerable proportion of institutional deliveries, it is important to assess the quality of maternity care offered by private sector healthcare facilities. This study seeks to fill that information gap by analysing baseline assessments conducted for the Manyata program, which aims to improve the quality of maternity care at private facilities. An observation checklist based on 16 clinical standards endorsed by the Federation of Obstetric and Gynaecological Societies of India (FOGSI) was used to assess 201 private sector healthcare facilities in Maharashtra, Jharkhand, and Uttar Pradesh. Data on facility characteristics came from profiles completed when facilities enrolled in Manyata. Differences in the mean number of standards met were analysed by facility characteristics and the availability of essential supplies. Around half (47.1%) of all nursing staff engaged in maternity care services at these private healthcare facilities were under qualified. The mean number of clinical standards met by facilities was 3.2 (SD 2.4). Facilities with a monthly delivery load between 20 and 50 met a significantly higher number of standards, as did facilities that had more than 70% of essential supplies available. Both these factors were also significant in a multiple linear regression analysis. The overall quality of maternity care in private healthcare facilities is poor in all three states, especially for clinical standards related to management of complications.
Automorphisms of Abelian Varieties and Principal Polarizations

The Narasimhan–Nori conjecture asks for a closed formula for the number of non-isomorphic principal polarizations of any given abelian variety. In this paper, we introduce a new algorithm that gives a lower bound on the number of non-isomorphic principal polarizations on any given abelian variety. We show, for example, that the Jacobian of the genus four underlying curve of Schoen’s I-WP minimal surface has at least 9 non-isomorphic principal polarizations. We also explore the Jacobians of Klein’s quartic, Fermat’s quartic, Bring’s curve, and more.
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning

Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the so-called false positives), obscuring alerts resulting from actual malicious activities. We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. Our approach assumes that the attacker knows the full state of the detection system and the defender's alert prioritization policy, and will dynamically choose an optimal attack. The first step of our approach is to capture the interaction between the defender and attacker in a game theoretic model. To tackle the computational complexity of solving this game to obtain a dynamic stochastic alert prioritization policy, we propose an adversarial reinforcement learning framework. In this framework, we use neural reinforcement learning to compute best response policies for both the defender and the adversary to an arbitrary stochastic policy of the other. We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender. We use case studies in network intrusion and fraud detection to demonstrate that our approach is effective in creating robust alert prioritization policies.1
Listening-Related Fatigue in Children With Unilateral Hearing Loss

Purpose Listening-related fatigue is an understudied construct that may contribute to the auditory, educational, and psychosocial problems experienced by children with unilateral hearing loss (UHL). Herein, we present an overview of listening-related fatigue in school-age children with hearing loss (CHL), with a focus on children with UHL. Method Following a review of research examining listening-related fatigue in adults and CHL, we present preliminary findings exploring the effects of unilateral and bilateral hearing loss on listening-related fatigue in children. For these exploratory analyses, we used data collected from our ongoing work developing and validating a tool, the Vanderbilt Fatigue Scale, for measuring listening-related fatigue in children. Presently, we are assessing 3 versions of the fatigue scale-child self-report, parent proxy, and teacher proxy. Using these scales, data have been collected from more than 900 participants. Data from children with unilateral and bilateral hearing loss and for children with no hearing loss are compared with adult Vanderbilt Fatigue Scale data. Results Results of our literature review and exploratory analyses suggest that adults and CHL are at increased risk for listening-related fatigue. Importantly, this increased risk was similar in magnitude regardless of whether the loss was unilateral or bilateral. Subjective ratings, based on child self-report and parent proxy report, were consistent, suggesting that children with unilateral and bilateral hearing loss experienced greater listening-related fatigue than children with no hearing loss. In contrast, results based on teacher proxy report were not sensitive to the effects of hearing loss. Conclusions Children with UHL are at increased risk for listening-related fatigue, and the magnitude of fatigue is similar to that experienced by children with bilateral hearing loss. Problems of listening-related fatigue in school-age CHL may be better identified by CHL themselves and their parents than by teachers and specialists working with the children.
Dynamic games for secure and resilient control system design

Modern control systems are featured by their hierarchical structure composed of cyber, physical and human layers. The intricate dependencies among multiple layers and units of modern control systems require an integrated framework to address cross-layer design issues related to security and resilience challenges. To this end, game theory provides a bottom-up modeling paradigm to capture the strategic interactions among multiple components of the complex system and enables a holistic view to understand and design cyber-physical-human control systems. In this review, we first provide a multi-layer perspective toward increasingly complex and integrated control systems and then introduce several variants of dynamic games for modeling different layers of control systems. We present game-theoretic methods for understanding the fundamental tradeoffs of robustness, security and resilience and developing a cross-layer approach to enhance the system performance in various adversarial environments. This review also includes three quintessential research problems that represent three research directions where dynamic game approaches can bridge between multiple research areas and make significant contributions to the design of modern control systems. The paper is concluded with a discussion on emerging areas of research that crosscut dynamic games and control systems.
Control of polymorphism during epitaxial growth of hyperferroelectric candidate LiZnSb on GaSb (111)B

A major challenge for ferroelectric devices is the depolarization field, which competes with and often destroys long-range polar order in the limit of ultrathin films. Recent theoretical predictions suggest a new class of materials, termed hyperferroelectics, that should be robust against the depolarization field and enable ferroelectricity down to the monolayer limit. Here, the authors demonstrate the epitaxial growth of hexagonal LiZnSb, one of the hyperferroelectric candidate materials, by molecular-beam epitaxy on GaSb (111)B substrates. Due to the high volatility of all three atomic species, they find that LiZnSb can be grown in an adsorption-controlled window, using an excess zinc flux. Within this window, the desired polar hexagonal phase is stabilized with respect to a competing cubic polymorph, as revealed by x-ray diffraction and transmission electron microscopy measurements. First-principles calculations show that for moderate amounts of epitaxial strain and moderate concentrations of Li vacancies, the cubic LiZnSb phase is lower in formation energy than the hexagonal phase, but only by a few millielectronvolts per formula unit. Therefore, they suggest that kinetics plays a role in stabilizing the desired hexagonal phase at low temperatures. Their results provide a path toward experimentally demonstrating ferroelectricity and hyperferroelectricity in a new class of ternary intermetallic compounds.
Activation of a nerve injury transcriptional signature in airway-innervating sensory neurons after lipopolysaccharide-induced lung inflammation

The lungs and the immune and nervous systems functionally interact to respond to respiratory environmental exposures and infections. The lungs are innervated by vagal sensory neurons of the jugular and nodose ganglia, fused together in smaller mammals as the jugular-nodose complex (JNC). Whereas the JNC shares properties with the other sensory ganglia, the trigeminal (TG) and dorsal root ganglia (DRG), these sensory structures express differential sets of genes that reflect their unique functionalities. Here, we used RNA sequencing (RNA-seq) in mice to identify the differential transcriptomes of the three sensory ganglia types. Using a fluorescent retrograde tracer and fluorescence-activated cell sorting, we isolated a defined population of airway-innervating JNC neurons and determined their differential transcriptional map after pulmonary exposure to lipopolysaccharide (LPS), a major mediator of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) after infection with gram-negative bacteria or inhalation of organic dust. JNC neurons activated an injury response program, leading to increased expression of gene products such as the G protein-coupled receptor Cckbr, inducing functional changes in neuronal sensitivity to peptides, and Gpr151, also rapidly induced upon neuropathic nerve injury in pain models. Unique JNC-specific transcripts, present at only minimal levels in TG, DRG, and other organs, were identified. These included TMC3, encoding for a putative mechanosensor, and urotensin 2B, a hypertensive peptide. These findings highlight the unique properties of the JNC and reveal that ALI/ARDS rapidly induces a nerve injury-related state, changing vagal excitability.
The Bouncing Penny and Nonholonomic Impacts

The evolution of a Lagrangian mechanical system is variational. Likewise, when dealing with a hybrid Lagrangian system (a system with discontinuous impacts), the impacts can also be described by variations. These variational impacts are given by the so-called Weierstrass-Erdmann corner conditions. Therefore, hybrid Lagrangian systems can be completely understood by variational principles.Unlike typical (unconstrained / holonomic) Lagrangian systems, nonholonomically constrained Lagrangian systems are not variational. However, by using the Lagrange-d’Alembert principle, nonholonomic systems can be described as projections of variational systems. This paper works out the analogous version of the Weierstrass-Erdmann corner conditions for nonholonomic systems and examines the billiard problem with a rolling disk.
Can chiral EFT give us satisfaction

We compare nuclear forces derived from chiral effective field theory (EFT) with those obtained from traditional (phenomenological and meson) models. By means of a careful analysis of paralleles and differences, we show that chiral EFT is superior to all earlier approaches in terms of both formal aspects and successful applications in ab initio calculations. However, in spite of the considerable progress made possible by chiral EFT, complete satisfaction cannot be claimed until outstanding problems – the renormalization issue being the most important one – are finally settled.
Far from equilibrium dynamics of tracer particles embedded in a growing multicellular spheroid

By embedding inert tracer particles (TPs) in a growing multicellular spheroid the local stresses on the cancer cells (CCs) can be measured. In order for this technique to be effective the unknown effect of the dynamics of the TPs on the CCs has to be elucidated to ensure that the TPs do not greatly alter the local stresses on the CCs. We show, using theory and simulations, that the self-generated (active) forces arising from proliferation and apoptosis of the CCs drive the dynamics of the TPs far from equilibrium. On time scales less than the division times of the CCs, the TPs exhibit sub-diffusive dynamics (the mean square displacement, ΔTP(t) ~ t^βTP with βTP 2) due to persistent directed motion for long times. In comparison, proliferation of the CCs randomizes their motion leading to superdiffusive behavior with αCC exceeding unity. Most importantly, αCC is not significantly affected by the TPs. Our predictions could be tested using in vitro imaging methods where the motion of the TPs and the CCs can be tracked.
Wilson Loops in Ising Lattice Gauge Theory

Wilson loop expectation in 4D $$\mathbb {Z}_2$$ lattice gauge theory is computed to leading order in the weak coupling regime. This is the first example of a rigorous theoretical calculation of Wilson loop expectation in the weak coupling regime of a 4D lattice gauge theory. All prior results are either inequalities or strong coupling expansions.
Very long-term follow-up of the anterior vaginal wall suspension procedure for incontinence and/or prolapse repair.

To report outcomes of the anterior vaginal wall suspension (AVWS) procedure for stress urinary incontinence (SUI) and/or anterior compartment prolapse (POP), with minimum 10-year follow-up. Following institutional review board approval, a database of patients with > 10-year follow-up after AVWS for bothersome SUI with early stage anterior compartment prolapse (stage ≤ 2) or symptomatic anterior compartment prolapse (stage > 2) was reviewed. Preoperative evaluation included validated questionnaires [Urogenital Distress Inventory-Short Form (UDI-6), Incontinence Impact Questionnaire-Short Form (IIQ-7), quality of life (QoL)], Pelvic Organ Prolapse Quantification (POP-Q), and voiding cystourethrography. Follow-up data was based on clinic visits or telephone interviews for patients not seen in the past 2 years. Telephone interviews used validated questionnaires and were conducted by a third party not involved in patient care. Failure was defined as reoperation for SUI or POP. The influence of lost to follow-up (LTF) was also analyzed. Between 1996 and 2008, 161 of 328 patients met study criteria, with follow-up from phone interviews (103) or clinic visits (58). The LTF patients were deceased (52), mentally disabled (5), or unreachable by telephone (110). Median follow-up was 13.5 years (IQR 11.5–17). Type of follow-up (clinic vs. phone) and uterine status (concomitant/prior/no hysterectomy) did not impact reoperation rates. Reoperation occurred in 23/161 (14%) and consisted of sacrocolpopexy (8), anterior colporrhaphy (5), injectable agents (8), fascial sling (2). The AVWS procedure to restore anterior vaginal support to the bladder neck and bladder base to correct SUI/POP can provide satisfactory and durable results.
DNA demethylation is a driver for chick retina regeneration.

Cellular reprogramming resets the epigenetic landscape to drive shifts in transcriptional programmes and cell identity. The embryonic chick can regenerate a complete neural retina, after retinectom...
Adaptive Submodular Ranking and Routing

Many applications of stochastic optimization involve making sequential decisions until some stopping criterion is satisfied. For example, in medical diagnosis, a doctor needs to perform an adaptive...