cssi/SciSciGPT-SciSciNet huggingface dataset:
Integral transforms and Drinfeld centers in derived algebraic geometry

We study the interaction between geometric operations on stacks and algebraic operations on their categories of sheaves. We work in the general setting of derived algebraic geometry: our basic objects are derived stacks X and their oo-categories QC(X) of quasicoherent sheaves. We show that for a broad class of derived stacks, called perfect stacks, algebraic and geometric operations on their categories of sheaves are compatible. We identify the category of sheaves on a fiber product with the tensor product of the categories of sheaves on the factors. We also identify the category of sheaves on a fiber product with functors between the categories of sheaves on the factors (thus realizing functors as integral transforms, generalizing a theorem of Toen for ordinary schemes). As a first application, for a perfect stack X, consider QC(X) with its usual monoidal tensor product. Then our main results imply the equivalence of the Drinfeld center (or Hochschild cohomology category) of QC(X), the trace (or Hochschild homology category) of QC(X) and the category of sheaves on the loop space of X. More generally, we show that the E_n-center and the E_n-trace (or E_n-Hochschild cohomology and homology categories respectively) of QC(X) are equivalent to the category of sheaves on the space of maps from the n-sphere into X. This directly verifies geometric instances of the categorified Deligne and Kontsevich conjectures on the structure of Hochschild cohomology. As a second application, we use our main results to calculate the Drinfeld center of categories of linear endofunctors of categories of sheaves. This provides concrete applications to the structure of Hecke algebras in geometric representation theory. Finally, we explain how all of the above results can be interpreted in the context of topological field theory.
Transport in Suspended Monolayer and Bilayer Graphene Under Strain: A New Platform for Material Studies

We develop two types of graphene devices based on nanoelectromechanical systems (NEMS), that allows transport measurement in the presence of in situ strain modulation. Different mobility and conductance responses to strain were observed for single layer and bilayer samples. These types of devices can be extended to other 2D membranes such as MoS2, providing transport, optical or other measurements with in situ strain.
Spark Level Sparsity and the $\ell_1$ Tail Minimization

Abstract Solving compressed sensing problems relies on the properties of sparse signals. It is commonly assumed that the sparsity s needs to be less than one half of the spark of the sensing matrix A, and then the unique sparsest solution exists, and is recoverable by l 1 -minimization or related procedures. We discover, however, a measure theoretical uniqueness exists for nearly spark-level sparsity from compressed measurements A x = b . Specifically, suppose A is of full spark with m rows, and suppose m 2 s m . Then the solution to A x = b is unique for x with ‖ x ‖ 0 ≤ s up to a set of measure 0 in every s-sparse plane. This phenomenon is observed and confirmed by an l 1 -tail minimization procedure, which recovers sparse signals uniquely with s > m 2 in thousands and thousands of random tests. We further show instead that the mere l 1 -minimization would actually fail if s > m 2 even from the same measure theoretical point of view.
Bag of Tricks for Image Classification with Convolutional Neural Networks

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms

We consider empirical risk minimization of linear predictors with convex loss functions. Such problems can be reformulated as convex-concave saddle point problems, and thus are well suitable for primal-dual first-order algorithms. However, primal-dual algorithms often require explicit strongly convex regularization in order to obtain fast linear convergence, and the required dual proximal mapping may not admit closed-form or efficient solution. In this paper, we develop both batch and randomized primal-dual algorithms that can exploit strong convexity from data adaptively and are capable of achieving linear convergence even without regularization. We also present dual-free variants of the adaptive primal-dual algorithms that do not require computing the dual proximal mapping, which are especially suitable for logistic regression.
Checking is Believing: Event-Aware Program Anomaly Detection in Cyber-Physical Systems

Securing cyber-physical systems (CPS) against malicious attacks is of paramount importance because these attacks may cause irreparable damages to physical systems. Recent studies have revealed that control programs running on CPS devices suffer from both control-oriented attacks (e.g., code-injection or code-reuse attacks) and data-oriented attacks (e.g., non-control data attacks). Unfortunately, existing detection mechanisms are insufficient to detect runtime data-oriented exploits, due to the lack of runtime execution semantics checking. In this work, we propose Orpheus , a new security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics. We first present a general method for reasoning cyber-physical execution semantics of a control program (i.e., causal dependencies between the physical context/event and program control flows), including the event identification and dependence analysis. As an instantiation of Orpheus , we then present a new program behavior model, i.e., the event-aware finite-state automaton ( e FSA). e FSA takes advantage of the event-driven nature of CPS control programs and incorporates event checking in anomaly detection. It detects data-oriented exploits if a specific physical event is missing along with the corresponding event dependent state transition. We evaluate our prototype’s performance by conducting case studies under data-oriented attacks. Results show that e FSA can successfully detect different runtime attacks. Our prototype on Raspberry Pi incurs a low overhead, taking 0.0001s for each state transition integrity checking, and 0.063s $\sim$ ∼ 0.211s for the cyber-physical contextual consistency checking.
ULTRAHIGH ENERGY COSMIC RAYS: REVIEW OF THE CURRENT SITUATION

We describe the current situation of the data on the highest energy particles in the Universe – the ultrahigh energy cosmic rays. The new results in the field come from the Telescope Array experiment in Utah, U.S.A. For this reason we concentrate on the results from these experiments and compare them to the measurements of the other two recent experiments, the High Resolution Fly’s Eye and the Southern Auger Observatory.
A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Network Simulator

Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has run-times plagued by wasted CPU cycles and unnecessary processes. Hardware-based simulation affords substantial speedup, but prior attempts at hardware-based biological simulation have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several hardware-based simulation schemes utilizing novel random update index generation techniques for step-based and round-based stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.
Selecting Microarchitecture Configuration of Processors for Internet of Things

The Internet of Things (IoT) makes use of ubiquitous internet connectivity to form a network of everyday physical objects for purposes of automation, remote data sensing and centralized management/control. IoT objects need to be embedded with processing capabilities to fulfill these services. The design of processing units for IoT objects is constrained by various stringent requirements, such as performance, power, thermal dissipation etc. In order to meet these diverse requirements, a multitude of processor design parameters need to be tuned accordingly. In this paper, we propose a temporally efficient design space exploration methodology which determines power and performance optimized microarchitecture configurations. We also discuss the possible combinations of these microarchitecture configurations to form an effective two-tiered heterogeneous processor for IoT applications. We evaluate our design space exploration methodology using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH2 benchmarks. The results show that our methodology determines microarchitecture configurations which are within 2.23–3.69 percent of the configurations obtained from fully exhaustive exploration while only exploring 3–5 percent of the design space. Our methodology achieves on average 24.16× speedup in design space exploration as compared to fully exhaustive exploration in finding power and performance optimized microarchitecture configurations for processors.
An analysis of dual-issue final-offer arbitration

We consider a final-offer arbitration problem between two players with two quantitative issues in dispute. Under reasonable assumptions we model the problem as a zero-sum two person game and show that a pair of pure strategies explicitly constructed are the unique minimax strategies for the two players.
Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation

This paper examines experimental design procedures used to develop surrogates of computational models, exploring the interplay between experimental designs and approximation algorithms. We focus on two widely used approximation approaches, Gaussian process (GP) regression and nonintrusive polynomial approximation. First, we introduce algorithms for minimizing a posterior integrated variance (IVAR) design criterion for GP regression. Our formulation treats design as a continuous optimization problem that can be solved with gradient-based methods on complex input domains without resorting to greedy approximations. We show that minimizing IVAR in this way yields point sets with good interpolation properties and that it enables more accurate GP regression than designs based on entropy minimization or mutual information maximization. Second, using a Mercer kernel/eigenfunction perspective on GP regression, we identify conditions under which GP regression coincides with pseudospectral polynomial approximation. ...
Fast Distribution To Real Regression

We study the problem of distribution to real regression, where one aims to regress a mapping f that takes in a distribution input covariate P 2 I (for a non-parametric family of distributionsI) and outputs a real-valued response Y = f(P ) + . This setting was recently studied in [15], where the \KernelKernel" estimator was introduced and shown to have a polynomial rate of convergence. However, evaluating a new prediction with the Kernel-Kernel estimator scales as ( N). This causes the dicult situation where a large amount of data may be necessary for a low estimation risk, but the computation cost of estimation becomes infeasible when the data-set is too large. To this end, we propose the Double-Basis estimator, which looks to alleviate this big data problem in two ways: rst, the Double-Basis estimator is shown to have a computation complexity that is independent of the number of of instances N when evaluating new predictions after training; secondly, the Double-Basis estimator is shown to have a fast rate of convergence for a general class of mappings f2F.
Quantum enhancements and biquandle brackets

We introduce a new class of quantum enhancements we call biquandle brackets, which are customized skein invariants for biquandle colored links. Quantum enhancements of biquandle counting invariants form a class of knot and link invariants that includes biquandle cocycle invariants and skein invariants such as the HOMFLY-PT polynomial as special cases, providing an explicit unification of these apparently unrelated types of invariants. We provide examples demonstrating that the new invariants are not determined by the biquandle counting invariant, the knot quandle or the knot group.
About Some Progressions

In this article one builds sets which have the following property: for any division in two subsets, at least one of these subsets contains at least three elements in arithmetic (or geometrical) progression.
Accelerating Imitation Learning with Predictive Models

Sample efficiency is critical in solving real-world reinforcement learning problems where agent-environment interactions can be costly. Imitation learning from expert advice has proved to be an effective strategy for reducing the number of interactions required to train a policy. Online imitation learning, which interleaves policy evaluation and policy optimization, is a particularly effective technique with provable performance guarantees. In this work, we seek to further accelerate the convergence rate of online imitation learning, thereby making it more sample efficient. We propose two model-based algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based on solving variational inequalities and MoBIL-Prox based on stochastic first-order updates. These two methods leverage a model to predict future gradients to speed up policy learning. When the model oracle is learned online, these algorithms can provably accelerate the best known convergence rate up to an order. Our algorithms can be viewed as a generalization of stochastic Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style analysis of performance.
Norm convergence of continuous-time polynomial multiple ergodic averages

For a jointly measurable probability-preserving action τ:ℝD↷(X,μ) and a tuple of polynomial maps pi:ℝ→ℝD, i=1,2,…,k, the multiple ergodic averages converge in L2(μ) as T→∞ for any f1,f2,…,fk∈L∞(μ). This confirms the continuous-time analog of the conjectured norm convergence of discrete polynomial multiple ergodic averages, which in its original formulation remains open in most cases. A proof of convergence can be given based on the idea of passing up to a sated extension of (X,μ,τ) in order to find a simple partially characteristic factor, similarly to the recent development of this idea for the study of related discrete-time averages, together with a new inductive scheme on tuples of polynomials. The new induction scheme becomes available upon changing the time variable in the above integral by some fractional power, and provides an alternative to Bergelson’s polynomial ergodic theorem induction, which has been the mainstay of positive results in this area in the past.
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.
Damped infinite energy solutions of the 3D Euler and Boussinesq equations

Abstract We revisit a family of infinite-energy solutions of the 3D incompressible Euler equations proposed by Gibbon et al. [9] and shown to blowup in finite time by Constantin [6] . By adding a damping term to the momentum equation we examine how the damping coefficient can arrest this blowup. Further, we show that similar infinite-energy solutions of the inviscid 3D Boussinesq system with damping can develop a singularity in finite time as long as the damping effects are insufficient to arrest the (undamped) 3D Euler blowup in the associated damped 3D Euler system.
SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.

Author(s): Hayes, Wayne B; Mamano, Nil | Abstract: Motivation:Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolated: each pairing is dependent on every other via the alignment itself. Existing measures fail to take into account the frequency of GO terms across networks, instead imposing arbitrary rules on when to allow GO terms. Results:Here we develop NetGO, a new measure that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without arbitrary cutoffs, instead downweighting GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO in alignments of predetermined quality and show that NetGO correlates with alignment quality better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measuresa feature not shared with existing GObased network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job at separating good alignments from bad ones. Availability and implementation:Available as part of SANA. Contact:whayes@uci.edu. Supplementary information:Supplementary data are available at Bioinformatics online.
Toward controlled generation of text

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible text sentences, whose attributes are controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders (VAEs) and holistic attribute discriminators for effective imposition of semantic structures. The model can alternatively be seen as enhancing VAEs with the wake-sleep algorithm for leveraging fake samples as extra training data. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns interpretable representations from even only word annotations, and produces short sentences with desired attributes of sentiment and tenses. Quantitative experiments using trained classifiers as evaluators validate the accuracy of sentence and attribute generation.
Energy Harvesting Two-Hop Communication Networks

Energy harvesting multihop networks allow for perpetual operation of low cost limited range wireless devices. Compared with their battery-operated counterparts, the coupling of energy and data causality constraints with half-duplex relay operation makes it challenging to operate such networks. In this paper, a throughput maximization problem for energy harvesting two-hop networks with decode-and-forward half-duplex relays is investigated. For a system with two parallel relays, various combinations of the following four transmission modes are considered: broadcast from the source, multiaccess from the relays, and successive relaying phases I and II. Optimal transmission policies for one and two parallel relays are studied under the assumption of noncausal knowledge of energy arrivals and finite size relay data buffers. The problem is formulated using a convex optimization framework, which allows for efficient numerical solutions and helps identify important properties of optimal policies. Numerical results are presented to provide throughput comparisons and to investigate the impact of multiple relays, size of relay data buffers, transmission modes, and energy harvesting on the throughput.
The Soergel category and the redotted Webster algebra

Abstract We describe a collection of graded rings which surject onto Webster rings for sl(2) and which should be related to certain categories of singular Soergel bimodules. In the first non-trivial case, we construct a categorical braid group action which categorifies the Burau representation.
Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets

Because of the vast volume of data being produced by today’s scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology simulation, such as the Hardware/Hybrid Accelerated Cosmology Code (HACC), where memory overhead constraints restrict compression to only one snapshot at a time, the lossy compression ratio is extremely limited because of the fairly low spatial coherence and high irregularity of the data. In this work, we propose a pattern-matching (similarity searching) technique to optimize the prediction accuracy and compression ratio of SZ lossy compressor on the HACC data sets. We evaluate our proposed method with different configurations and compare it with state-of-the-art lossy compressors. Experiments show that our proposed optimization approach can improve the prediction accuracy and reduce the compressed size of quantization codes compared with SZ. We present several lessons useful for future research involving pattern-matching techniques for lossy compression.
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization

Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.
On homology of complements of compact sets in Hilbert Cube

Abstract We introduce the notion of cohomologically weakly infinite-dimensional spaces and show the acyclicity of the complement Q ∖ X in the Hilbert cube Q of a cohomologically weakly infinite-dimensional compactum X. As a corollary we obtain the acyclicity of the complement results when (a) X is weakly infinite-dimensional; (b) X has finite cohomological dimension.
Analysis of Approximate Message Passing With Non-Separable Denoisers and Markov Random Field Priors

Approximate message passing (AMP) is a class of low-complexity, scalable algorithms for solving high-dimensional linear regression tasks where one wishes to recover an unknown signal from noisy, linear measurements. AMP is an iterative algorithm that performs estimation by updating an estimate of the unknown signal at each iteration and the performance of AMP (quantified, for example, by the mean squared error of its estimates) depends on the choice of a “denoiser” function that is used to produce these signal estimates at each iteration. An attractive feature of AMP is that its performance can be tracked by a scalar recursion referred to as state evolution. Previous theoretical analysis of the accuracy of the state evolution predictions has been limited to the use of only separable denoisers or block-separable denoisers, a class of denoisers that underperform when sophisticated dependencies exist between signal entries. Since signals with entrywise dependencies are common in image/video-processing applications, in this work we study the high-dimensional linear regression task when the dependence structure of the input signal is modeled by a Markov random field prior distribution. We provide a rigorous analysis of the performance of AMP, demonstrating the accuracy of the state evolution predictions, when a class of non-separable sliding-window denoisers is applied. Moreover, we provide numerical examples where AMP with sliding-window denoisers can successfully capture local dependencies in images.
Jones-Wassermann subfactors for modular tensor categories

Abstract The representation category of a conformal net is a unitary modular tensor category. We investigate the reconstruction program: whether all unitary modular tensor categories are representation categories of conformal nets. We give positive evidence: the fruitful theory of multi-interval Jones-Wassermann subfactors on conformal nets is also true for modular tensor categories. We construct multi-interval Jones-Wassermann subfactors for unitary modular tensor categories. We prove that these subfactors are symmetrically self-dual. It generalizes and categorifies the self-duality of finite abelian groups. We call this duality the modular self-duality, because the modularity of the modular tensor category appears in a crucial way. For each unitary modular tensor category, we obtain a sequence of unitary fusion categories. The cyclic group case gives examples of Tambara-Yamagami categories.
UCLA Secure Unix

There has been considerable interest for some time in developing an operating system which could be conclusively shown secure, in the sense that the information stored on behalf of a heterogeneous user population was safely protected from unauthorized access or modification, even in the face of skilled attempts to do so. Early attempts to attain this goal consisted largely of auditing an existing system through attempts at circumventing the controls, and then revising the implementation code to block any successful paths that were found. Unfortunately, this approach failed to produce a secure system, largely because third generation operating systems contain so many errors that "penetration audits" followed by patches inevitably led to a system whose controls were still easily penetrated.
Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2A) in 50% of the cases.
Distribution System State Estimation in the Presence of High Solar Penetration

Low-to-medium voltage distribution networks are experiencing rising levels of distributed energy resources, including renewable generation, along with improved sensing, communication, and automation infrastructure. As such, state estimation methods for distribution systems are becoming increasingly relevant as a means to enable better control strategies that can both leverage the benefits and mitigate the risks associated with high penetration of variable and uncertain distributed generation resources. The primary challenges of this problem include modeling complexities (nonlinear, nonconvex power-flow equations), limited availability of sensor measurements, and high penetration of uncertain renewable generation. This paper formulates the distribution system state estimation as a nonlinear, weighted, least squares problem, based on sensor measurements as well as forecast data (both load and generation). We investigate the sensitivity of state estimator accuracy to (load/generation) forecast uncertainties, sensor accuracy, and sensor coverage levels.
Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors

We consider the sequential change-point detection for asynchronous multi-sensors, where each sensor observe a signal (due to change-point) at different times. We propose an asynchronous Subspace-CUSUM procedure based on jointly estimating the unknown signal waveform and the unknown relative delays between sensors. Using the estimated delays, we can align signals and use the subspace to combine multiple sensor observations. We derive the optimal drift parameter for the proposed procedure, and characterize the relationship between the expected detection delay, average run length (of false alarms), and the energy of the time-varying signal. We demonstrate the good performance of the proposed procedure using simulation and real data. We also demonstrate that the proposed procedure outperforms the well-known "one-shot procedure" in detecting weak and asynchronous signals.
A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations

Abstract For the problem of sparse recovery, it is widely accepted that nonconvex minimizations are better than l 1 penalty in enhancing the sparsity of solution. However, to date, the theory verifying that nonconvex penalties outperform (or are at least as good as) l 1 minimization in exact, uniform recovery has mostly been limited to separable cases. In this paper, we establish general recovery guarantees through null space conditions for nonconvex, non-separable regularizations, which are slightly less demanding than the standard null space property for l 1 minimization.
Corporate Risk Management under Information Asymmetry

This paper examines the financial and operational hedging activities of US pharma- ceutical and biotech firms that are subject to a high level of information asymmetry stemming from R&D investments during 2001-2006. We find evidence in support of the information asymmetry hypothesis ` a la Froot, Scharfstein and Stein (1993) that hedging helps mitigate the under-investment problem. Specifically, we find that the use of financial derivatives is associated with greater firm value and that the value enhancement is larger for firms subject to greater information asymmetry and better growth opportunities. There is a synergy between financial hedging and operational hedging where the latter is used to counter product development risk. The results are robust with respect to alternative performance measures, industry-specific growth measures, and the endogeneity problem. Our work is differentiated from existing studies that examined commodity-based industries without addressing information asymmetry.
Predictors of Groin Access Pseudoaneurysm Complication: A 10-Year Institutional Experience.

Objective:In clinical practice, the incidence of femoral pseudoaneurysms requiring repair is small, but at a tertiary care center, the repair rate is higher due to referrals. We sought to specifica...
On the efficiency of software implementations of lightweight block ciphers from the perspective of programming languages

Abstract Lightweight block ciphers are primarily designed for resource constrained devices. However, due to service requirements of large-scale IoT networks and systems, the need for efficient software implementations cannot be ruled out. A number of studies have compared software implementations of different lightweight block ciphers on a specific platform but to the best of our knowledge, this is the first attempt to benchmark various software implementations of a single lightweight block cipher across different programming languages and platforms in the cloud architecture. In this paper, we defined six lookup-table based software implementations for lightweight block ciphers with their characteristics ranging from memory to throughput optimized variants. We carried out a thorough analysis of the two costs associated with each implementation (memory and operations) and discussed possible trade-offs in detail. We coded all six types of implementations for three key settings (64, 80, 128 bits) of LED (a lightweight block cipher) in four programming languages (Java, C#, C++, Python). We highlighted the impact of choice relating to implementation type, programming language, and platform by benchmarking the seventy-two implementations for throughput and software efficiency on 32 & 64-bit platforms for two major operating systems (Windows & Linux) on Amazon Web Services Cloud. The results showed that these choices can affect the efficiency of a cryptographic primitive by a factor as high as 400.
The human risks of bias in medical and rehabilitation research and practice: the eight Is.

Background The human mind is liable to make biased interpretations. The biomedical paradigm supposes that an objective truth can be reached with rigorous scientific methods. Human risk of bias is defined in this paper as threats to validity of study results that cannot be controlled even by rigorous scientific methods. Aim To identify categories of potential human risks of biases in quantitative medical and rehabilitation practice and research; and to review the available evidence of human risks of biases in each of these categories. Design A narrative review. Results Eight potential categories of human risk of bias (HRoB) in clinical and rehabilitation practice and research were identified: identity, integrity, independence, intelligence, ideology, interest, incentive and inequity. There is evidence that conflict of interest, deficient integrity and inequity contribute to biased scientific interpretations; and that due to inequity the evidence of effectiveness of interventions for disadvantaged patient groups is scarce. There is also evidence that biased decisions can occur in health care practice due to the state of inequity. Discussion As all categories of HRoB's start with the letter 'I', these are named the eight Is. The categories overlap with each other. Studies assessing the impacts of biases related to conflict of interests, deficient integrity and inequity were identified. However, other potential human risks of biases have been studied very little or not at all. Conclusions The human beings' liability to biased thinking and all categories of HRoB should be recognized in clinical and rehabilitation practice and research, and use the best verified means to reduce the HRoB. More research is needed, particularly on how to reduce HRoB in medical practice and quantitative clinical research. These actions should be considered congruent to those aiming to increase the validity of the scientific method. Clinical rehabilitation impact The human risk of bias should be considered in assessment of evidence from clinical epidemiology as well as in clinical praxis.
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation.

Deep learning has largely reduced the need for manual feature selection in image segmentation. Nevertheless, network architecture optimization and hyperparameter tuning are mostly manual and time consuming. Although there are increasing research efforts on network architecture search in computer vision, most works concentrate on image classification but not segmentation, and there are very limited efforts on medical image segmentation especially in 3D. To remedy this, here we propose a framework, SegNAS3D, for network architecture search of 3D image segmentation. In this framework, a network architecture comprises interconnected building blocks that consist of operations such as convolution and skip connection. By representing the block structure as a learnable directed acyclic graph, hyperparameters such as the number of feature channels and the option of using deep supervision can be learned together through derivative-free global optimization. Experiments on 43 3D brain magnetic resonance images with 19 structures achieved an average Dice coefficient of 82%. Each architecture search required less than three days on three GPUs and produced architectures that were much smaller than the state-of-the-art manually created architectures.
An intelligent assistant for financial hedging

Problems in Finance, particularly those involving risk assessmentand management, have been slow to yield toexpert systems technology for two reasons. First, expertreasoning in such problems is often based on âfirst principles"instead of âsituation-action" rules that characterizemost expert systems. Secondly, the knowledge involved,such as that about financial instruments, is constantlychanging. This would make it extremely difficultto keep a rule-base accurate. We have developed a representationin the domain of financial hedging that hasthe following characteristics. First, it allows for reasoningqualitatively based on first principles using the fundamentalquantitative valuation models that characterizeeach instrument. Secondly, it uses object oriented conceptsand inheritance to minimize the effort needed toset up the knowledge base and keep it current. Thirdly,it includes a calculus for derivation of qualitative knowledgeof "one-dimensional-order", which allows it to solveproblems where optimality constraints are qualitative.
Conformal blocks attached to twisted groups

Geometric representation theory allows to associate to each simple and simply connected algebraic group G over a field and to a positive integer, a vector bundle, called the sheaf of conformal blocks, over M_g, the stack of curves of genus g. This thesis generalizes this construction to the situation in which G is replaced by a twisted group which depends on covering data. Using the methods described by Looijenga in the untwisted case, we prove that the main features of the classical sheaf of conformal block still hold in this more general setting. In particular we describe the WZW connection, the propagation of vacua and the fusion rules.
Remarks on a paper by Gavrilov: Grad–Shafranov equations, steady solutions of the three dimensional incompressible Euler equations with compactly supported velocities, and applications

We describe a method to construct smooth and compactly supported solutions of 3D incompressible Euler equations and related models. The method is based on localizable Grad–Shafranov equations and is inspired by the recent result (Gavrilov in A steady Euler flow with compact support. Geom Funct Anal 29(1):90–197, [Gav19]).
Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.
Spheres of legislation: polarization and most influential nodes in behavioral context

Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and apply unsupervised machine learning techniques, such as fuzzy clustering algorithms and generative models, to identify spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks to gain an insight into the role played by the spheres of legislation in various interesting constructs like polarization and most influential nodes? To this end, we apply both game-theoretic and social network analysis techniques. In particular, we show that game-theoretic notion of most influential nodes brings out the strategic aspects of interactions like bipartisan grouping, which structural centrality measures fail to capture.
Reverse Warburg effect-related mitochondrial activity and 18F-FDG uptake in invasive ductal carcinoma

Abstract Background We evaluated the relationship between 18F-FDG uptake and mitochondrial activity in cancer cells and investigated the prognostic implications thereof in patients with invasive ductal carcinoma of the breast (IDCB). Methods In total, 150 consecutive patients with IDCB who underwent preoperative 18F-FDG PET/CT followed by curative surgical resection were retrospectively enrolled. Mitochondrial activity of cancer cells was assessed based on translocase of outer mitochondrial membrane 20 (TOMM20) expression and cytochrome C oxidase (COX) activity. Pearson’s correlation analysis was used to assess the relationship between SUVmax of the primary tumour (pSUVmax) and mitochondrial activity. Clinicopathological factors, including pSUVmax, histological grade, expression of ER, PR, HER2, and TOMM20, and COX activity, were assessed for prediction of progression-free survival (PFS). The Kaplan–Meier method and Cox proportional hazards model were used for the survival analysis. Results Sixteen of the 150 subjects (10.7%) showed progression during the follow-up period. pSUVmax correlated significantly and positively with TOMM20 expression and COX activity. Univariate analysis revealed that pSUVmax, TOMM20 expression, COX activity, ER and PR status, and histologic grade were significantly associated with PFS. Multivariate analysis revealed a significant difference in pSUVmax (HR, 4.50; 95% CI, 1.43–14.17; P = 0.010). Conclusions There was a significant positive correlation between 18F-FDG uptake and mitochondrial activity of cancer cells in patients with IDCB. Increased 18F-FDG uptake and mitochondrial activity were significantly associated with a shorter PFS. Therefore, assessment of preoperative 18F-FDG uptake and post-surgical mitochondrial activity in patients with IDCB could be used as marker for prediction of PFS. Legal entity responsible for the study Choi Byung Wook (Daegu Catholic University Medical Center). Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.
The information content of real operating performance measures from the airline industry

Abstract I use daily passenger flight delay data for all publicly held domestic US airlines from 1996 to 2016 to answer fundamental questions about the efficiency of market participants, accounting measures, and analysts in assessing the firm's real operating performance. Airline performance is priced in the cross-section of cumulative market- and industry-adjusted returns, with significant drift. Quarterly aggregates of performance predict post-earnings announcement drift. The sensitivity of individual airline operations to those of the entire industry, measured using a beta approach, carries a negative premium consistent with systemic operating risk being a liability rather than a positively priced risk factor with implications for the accounting beta literature. Return on sales reflects the quarter's real operating performance better than other common ROA measures, contributing to the measure selection literature.
Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real-time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision-based systems to avoid hazards as well as plan safe routes. However, vision-based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision-based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this work, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared to traditional fixed-sensor configurations.