cssi/SciSciGPT-SciSciNet huggingface dataset:
De novo identification of essential protein domains from CRISPR-Cas9 tiling-sgRNA knockout screens

High-throughput CRISPR-Cas9 knockout screens using a tiling-sgRNA design permit in situ evaluation of protein domain function. Here, to facilitate de novo identification of essential protein domains from such screens, we propose ProTiler, a computational method for the robust mapping of CRISPR knockout hyper-sensitive (CKHS) regions, which refer to the protein regions associated with a strong sgRNA dropout effect in the screens. Applied to a published CRISPR tiling screen dataset, ProTiler identifies 175 CKHS regions in 83 proteins. Of these CKHS regions, more than 80% overlap with annotated Pfam domains, including all of the 15 known drug targets in the dataset. ProTiler also reveals unannotated essential domains, including the N-terminus of the SWI/SNF subunit SMARCB1, which is validated experimentally. Surprisingly, the CKHS regions are negatively correlated with phosphorylation and acetylation sites, suggesting that protein domains and post-translational modification sites have distinct sensitivities to CRISPR-Cas9 mediated amino acids loss. Tiling-sgRNA designs allow the in situ evaluation of protein domain functions. Here the authors present ProTiler - a computational method to predict CRISPR knockout hyper-sensitive regions, revealing previously unannotated domains.
Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics

We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the substantial literature of ChR variants to train statistical models for the design of high-performance ChRs. With Gaussian process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with high light sensitivity. Three of these, ChRger1-3, enable optogenetic activation of the nervous system via systemic transgene delivery. ChRger2 enables light-induced neuronal excitation without fiber-optic implantation; that is, this opsin enables transcranial optogenetics.
Interface asymptotics of Partial Bergman kernels around a critical level

In a recent series of articles, the authors have studied the transition behavior of partial Bergman kernels $\Pi_{k, [E_1, E_2]} (z,w)$ and the associated DOS (density of states) $\Pi_{k, [E_1, E_2]} (z)$ across the interface $\mathcal{C}$ between the allowed and forbidden regions. Partial Bergman kernels are Toeplitz Hamiltonians quantizing Morse functions $H : M \to \mathbb{R}$ on a Kahler manifold. The allowed region is $H^{-1} ([E_1, E_2])$ and the interface $\mathcal{C}$ is its boundary. In prior articles it was assumed that the endpoints $E_j$ were regular values of $H$. This article completes the series by giving parallel results when an endpoint is a critical value of $H$. In place of the Erf scaling asymptotics in a $k^{-\frac{1}{2}}$ tube around $\mathcal{C}$ for regular interfaces, one obtains $\delta$-asymptotics in $k^{-\frac{1}{4}}$ tubes around singular points of a critical interface. In $k^{-\frac{1}{2}}$ tubes, the transition law is given by the osculating metaplectic propagator.
Text2Scene: Generating Compositional Scenes From Textual Descriptions

In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.
Commonly Used Insect Repellents Hide Human Odors from Anopheles Mosquitoes

The mode of action for most mosquito repellents is unknown. This is primarily due to the difficulty in monitoring how the mosquito olfactory system responds to repellent odors. Here, we used the Q-system of binary expression to enable activity-dependent Ca2+ imaging in olfactory neurons of the African malaria mosquito Anopheles coluzzii. This system allows neuronal responses to common insect repellents to be directly visualized in living mosquitoes from all olfactory organs, including the antenna. The synthetic repellents N,N-diethyl-meta-toluamide (DEET) and IR3535 did not activate Anopheles odorant receptor co-receptor (Orco)-expressing olfactory receptor neurons (ORNs) at any concentration, and picaridin weakly activated ORNs only at high concentrations. In contrast, natural repellents (i.e. lemongrass oil and eugenol) strongly activated small numbers of ORNs in the Anopheles mosquito antennae at low concentrations. We determined that DEET, IR3535, and picaridin decrease the response of Orco-expressing ORNs when these repellents are physically mixed with activating human-derived odorants. We present evidence that synthetic repellents may primarily exert their olfactory mode of action by decreasing the amount of volatile odorants reaching ORNs. These results suggest that synthetic repellents disruptively change the chemical profile of host scent signatures on the skin surface, rendering humans invisible to Anopheles mosquitoes.
Set partitions without blocks of certain sizes

Abstract We give an asymptotic estimate for the number of partitions of a set of n elements, whose block sizes avoid a given set S of natural numbers. As an application, we derive an estimate for the number of partitions of a set with n elements, which have the property that its blocks can be combined to form subsets of any size between 1 and n .
Multiscale analysis of interlaminar stresses near a free-edge in a [±45/0/90]s laminate:

A multiscale model for a [±45/0/90]s tape laminate under uniaxial extension was used to investigate the effect of modeling the heterogeneous microstructure near a free-edge. A random fiber arrangem...
A Continuous-Time Analysis of Distributed Stochastic Gradient

We analyze the effect of synchronization on distributed stochastic gradient algorithms. By exploiting an analogy with dynamical models of biological quorum sensing, where synchronization between ag...
A Minimalist Proof of the Law of Quadratic Reciprocity

This proof is similar to one of the proofs of Eisenstein [1], albeit shorter.Let Sn(t)=∑x1+x2+…+xn=t(x1x2⋯xnp) , where t,xi∈Zp and (ap) is the Legendre symbol. Note that for an odd n and any a∈Zp/{...
Approximating the Geometric Edit Distance

Edit distance is a measurement of similarity between two sequences such as strings, point sequences, or polygonal curves. Many matching problems from a variety of areas, such as signal analysis, bioinformatics, etc., need to be solved in a geometric space. Therefore, the geometric edit distance (GED) has been studied. In this paper, we describe the first strictly sublinear approximate near-linear time algorithm for computing the GED of two point sequences in constant dimensional Euclidean space. Specifically, we present a randomized (O(n\log^2n)) time (O(\sqrt n))-approximation algorithm. Then, we generalize our result to give a randomized $\alpha$-approximation algorithm for any $\alpha\in [\sqrt{\log n}, \sqrt{n / \log n}]$, running in time $O(n^2/\alpha^2 \log n)$. Both algorithms are Monte Carlo and return approximately optimal solutions with high probability.
Carbon-deficient Red Giants

Carbon-deficient red giants (CDRGs) are a rare class of peculiar red giants, also called "weak G-band" or "weak-CH" stars. Their atmospheric compositions show depleted carbon, a low 12C/13C isotopic ratio, and an overabundance of nitrogen, indicating that the material at the surface has undergone CN-cycle hydrogen-burning. I present Stromgren uvby photometry of nearly all known CDRGs. Barium stars, having an enhanced carbon abundance, exhibit the "Bond-Neff effect"--a broad depression in their energy distributions at ~4000 A, recently confirmed to be due to the CH molecule. This gives Ba II stars unusually low Stromgren c1 photometric indices. I show that CDRGs, lacking CH absorption, exhibit an "anti-Bond-Neff effect": higher c1 indices than normal red giants. Using precise parallaxes from Gaia DR2, I plot CDRGs in the color-magnitude diagram (CMD) and compare them with theoretical evolution tracks. Most CDRGs lie in a fairly tight clump in the CMD, indicating initial masses in the range ~2 to 3.5 Msun, if they have evolved as single stars. It is unclear whether they are stars that have just reached the base of the red-giant branch and the first dredge-up of CN-processed material, or are more highly evolved helium-burning stars in the red-giant clump. About 10% of CDRGs have higher masses of ~4 to 4.5 Msun, and exhibit unusually high rotational velocities. I show that CDRGs lie at systematically larger distances from the Galactic plane than normal giants, possibly indicating a role of binary mass-transfer and mergers. CDRGs continue to present a major puzzle for our understanding of stellar evolution.
Attack Analysis and Resilient Control Design for Discrete-Time Distributed Multi-Agent Systems

This work analyzes the adverse effects of cyber-physical attacks on discrete-time distributed multi-agent systems, and proposes a mitigation approach for attacks on sensors and actuators. First, we show how an attack on a single node snowballs into a network-wide attack and even destabilizes the entire system. Next, to overcome the adversarial effects of attacks on sensors and actuators, a distributed adaptive attack compensator is designed by estimating the normal expected behavior of agents. The adaptive attack compensator is augmented with the controller and it is shown that the proposed controller achieves secure consensus in the presence of the attacks on sensors and actuators. No restrictive assumption on the number of agents under adversarial input is assumed. Moreover, it recovers compromised agents under actuator attacks and avoids propagation of attacks on sensors without discarding information from the compromised agents. Finally, numerical simulations validate the effectiveness of the presented theoretical contributions on a network of Sentry autonomous underwater vehicles.
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects—an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus .
Nonparametric adaptive CUSUM chart for detecting arbitrary distributional changes

Nonparametric control charts that can detect arbitrary distributional changes are highly desirable due to their flexibility to adapt to different distributional assumptions and changes. However, mo...
On the Dynamical Foundation of Multifractality

Abstract The crucial aspect of this demonstration is the discovery of renewal events, hidden in the computed dynamics of a multifractal metronome, which enables the replacement of the phenomenon of strong anticipation with a time delayed cross-correlation between the driven and the driving metronome. We establish that the phenomenon of complexity matching, which is the theme of an increasing number of research groups, has two distinct measures. One measure is the sensitivity of a complex system to environmental multifractality; another is the level of information transfer, between two complex networks at criticality. The cross-correlation function is evaluated in the ergodic long-time limit, but its delayed maximal value is the signature of information transfer occurring in the non ergodic short-time regime. It is shown that a more complex system transfers its multifractality to a less complex system while the reverse case is not possible.
BlazeIt: optimizing declarative aggregation and limit queries for neural network-based video analytics

Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of an object's position and type in video, they are computationally expensive and require complex, imperative deployment code to answer queries. Prior work uses approximate filtering to reduce the cost of video analytics, but does not handle two important classes of queries, aggregation and limit queries; moreover, these approaches still require complex code to deploy. To address the computational and usability challenges of querying video at scale, we introduce BlazeIt, a system that optimizes queries of spatiotemporal information of objects in video. BlazeIt accepts queries via FrameQL, a declarative extension of SQL for video analytics that enables video-specific query optimization. We introduce two new query optimization techniques in BlazeIt that are not supported by prior work. First, we develop methods of using NNs as control variates to quickly answer approximate aggregation queries with error bounds. Second, we present a novel search algorithm for cardinality-limited video queries. Through these these optimizations, BlazeIt can deliver up to 83X speedups over the recent literature on video processing.
Controlled short time large scale synthesis of magnetic cobalt nanoparticles on carbon nanotubes by flash annealing

Nanopatterned arrays of discrete cobalt nanostructures showing characteristic parameter-dependent sizes are formed from continuous thin films on a carbon nanotube substrate using millisecond pulsed intense UV light. The nanoparticles exhibit ferromagnetic behavior with magnetic remanence and coercivity depending on the particle size. The end-state particle size is shown to be a function of initial thin film thickness and excitation energy and is therefore tunable. The evolutionary process from continuous thin films to a discrete morphology is thermodynamically driven by the large surface energy difference between metastable thin films and the underlying carbon nanotube substrate. Evidence of the Danielson model of the dewetting process is observed. These arrays can find applications as platforms for the self-assembly of magnetically susceptible materials, such as iron or nickel nanostructures, into a conduction matrix for applications in energy extraction from a latent heat storage device.
Analysis and improvement of the hot disk transient plane source method for low thermal conductivity materials

Abstract The hot disk transient plane source (TPS) method is a widely used standard technique (ISO 22007–2) for characterizing the thermal properties of materials, especially the thermal conductivity, k. Despite its well-established reliability for a wide variety of common materials, the hot disk TPS method is also known to suffer from substantial systematic errors when applied to low-k thermal insulation materials, because of the discrepancies between the idealized model used for data analysis and the actual heat transfer process. Here, we present a combined numerical and experimental study of the influence of the geometry of the hot disk sensor on the measured k value of low-k materials. We demonstrate that the error is strongly affected by the finite thickness and thermal mass of the sensor's insulation layer as well as the corresponding increase of the effective heater size beyond the radius of the embedded metal heater itself. We further numerically investigate the dependence of the error on the sample thermal properties, confirming that the errors are worse in low-k samples. A simple polynomial correction function is provided based on the numerical error analysis, which converts the apparent (erroneous) result from a standard hot disk TPS measurement to a more accurate value of k. To experimentally validate the conclusions from numerical simulations, standard polyimide (Kapton) sensors are systematically optimized (thinned) by etching and used to measure low-k materials, including a standard polystyrene foam, a commercial Airloy® x56 aerogel, and a commercial Hydrophobic Silica Disk aerogel from Aerogel Technologies, LLC. The experimental results clearly demonstrate the strong influence of the sensor thickness. The k results of these samples obtained using either the optimized sensor or the pristine sensor then corrected with the corresponding polynomial correction functions are in good agreement with the values measured independently using a steady-state heat flowmeter (HFM) method: whereas the raw values measured with the pristine sensor are in error by 35% and 40% compared to the HFM reference values for the Airloy® x56 and the hydrophobic aerogel, respectively, the correction function greatly reduces those errors to
Private Pliable Index Coding

The Pliable Index CODing (PICOD) problem is a variant of the Index Coding (IC) problem, where the desired messages by the users, who are equipped with message side information, are part of the optimization. This paper studies the PICOD problem where users are subject to a privacy constraint. In particular, the following special class of private PICODs is investigated: 1) the side information structure is circular, and 2) each user can decode one and only one message. The first condition is a special case of the “circular-arc network topology hypergraph” class of PICOD studied in [6], for which an optimal solution was given without the privacy constraint. The second condition was first studied in [9] and was motivated by the need to keep content private in some distribution networks. This paper proposes both converse and achievable bounds. The proposed achievable scheme not only strictly outperforms the existing one for some values of the system parameters, but it is also information theoretically optimal in some settings. For the remaining cases, the proposed linear code is shown to require at most one more transmission than the best possible linear code.
CFT sewing as the dual of AdS cut-and-paste

The CPT map allows two states of a quantum field theory to be sewn together over CPT-conjugate partial Cauchy surfaces R1, R2 to make a state on a new spacetime. We study the holographic dual of this operation in the case where the original states are CPT-conjugate within R1, R2 to leading order in the bulk Newton constant G, and where the bulk duals are dominated by classical bulk geometries g1, g2. For states of fixed area on the R1, R2 HRT-surfaces, we argue that the bulk geometry g1#g2 dual to the newly sewn state is given by deleting the entanglement wedges of R1, R2 from g1, g2, gluing the remaining complementary entanglement wedges of $$ {\overline{R}}_1,{\overline{R}}_2 $$ together across the HRT surface, and solving the equations of motion to the past and future. The argument uses the bulk path integral and assumes it to be dominated by a certain natural saddle. For states where the HRT area is not fixed, the same bulk cut-and-paste is dual to a modified sewing that produces a generalization of the canonical purification state $$ \sqrt{\rho } $$ discussed recently by Dutta and Faulkner. Either form of the construction can be used to build CFT states dual to bulk geometries associated with multipartite reflected entropy.
Long-term variability of electron radiation dose in geosynchronous orbit

Abstract : A neural network that was developed to model the temporal variations of relativistic (>3 MeV) electrons at geosynchronous orbit has been used to estimate the long-term variability of the radiation dose to geosynchronous spacecraft. The input to the neural network consists of 10 consecutive days of the daily sum of the planetary magnetic index Epsilon Kp. The output is an estimate of the daily-averaged electron flux for the tenth day. The model was used to compute the daily- averaged electron flux for energies >3 MeV for each day from January 1933 through December 1988. Assuming that the average energy spectrum is independent of time, the annual radiation dose of electrons >300 keV is obtained by scaling the model output to 300 keV and summing the daily-averaged flux for each year. The annual dose is found to vary from 0.25 3.4 times its average value. The dose is found to correlate only weakly with the annual mean sunspot number. The results are also applicable to studies of the effect on the middle atmosphere of the long-term variability of the precipitation of these electrons into the atmosphere.
Learning Model Predictive Control for Connected Autonomous Vehicles

A Learning Model Predictive Controller (LMPC) is presented and tailored to platooning and Connected Autonomous Vehicles (CAVs) applications. The proposed controller builds on previous work on nonlinear LMPC, adapting its architecture and extending its capability to (a) handle dynamic environments and (b) account for data-driven decision variables that derive from an unknown or unknowable function. The paper presents the control design approach, and shows how to recursively construct an outer loop candidate trajectory and an inner iterative LMPC controller that converges to an optimal strategy over both model-driven and data-driven variables. Simulation results show the effectiveness of the proposed control logic.
Secret Key Agreement from Correlated Data, with No Prior Information.

A fundamental question that has been studied in cryptography and in information theory is whether two parties can communicate confidentially using exclusively an open channel. We consider the model in which the two parties hold inputs that are correlated in a certain sense. This model has been studied extensively in information theory, and communication protocols have been designed which exploit the correlation to extract from the inputs a shared secret key. However, all the existing protocols are not universal in the sense that they require that the two parties also know some attributes of the correlation. In other words, they require that each party knows something about the other party's input. We present a protocol that does not require any prior additional information. It uses space-bounded Kolmogorov complexity to measure correlation and it allows the two legal parties to obtain a common key that looks random to an eavesdropper that observes the communication and is restricted to use a bounded amount of space for the attack. Thus the protocol achieves complexity-theoretical security, but it does not use any unproven result from computational complexity. On the negative side, the protocol is not efficient in the sense that the computation of the two legal parties uses more space than the space allowed to the adversary.
Effective Low-Energy Theories and QCD Dirac Spectra

We analyze the smallest Dirac eigenvalues by formulating an effective theory for the Dirac spectrum. We find that in a domain where the kinetic term of the effective theory can be ignored, the Dirac eigenvalues are distributed according to a Random Matrix Theory with the global symmetries of the QCD partition function. The kinetic term provides information on the slope of the average spectral density of the Dirac operator. In the second half of this lecture we interpret quenched QCD Dirac spectra (with eigenvalues scattered in the complex plane) in terms of an effective low energy theory.
Harmonized global maps of above and belowground biomass carbon density in the year 2010

Remotely sensed biomass carbon density maps are widely used for myriad scientific and policy applications, but all remain limited in scope. They often only represent a single vegetation type and rarely account for carbon stocks in belowground biomass. To date, no global product integrates these disparate estimates into an all-encompassing map at a scale appropriate for many modelling or decision-making applications. We developed an approach for harmonizing vegetation-specific maps of both above and belowground biomass into a single, comprehensive representation of each. We overlaid input maps and allocated their estimates in proportion to the relative spatial extent of each vegetation type using ancillary maps of percent tree cover and landcover, and a rule-based decision schema. The resulting maps consistently and seamlessly report biomass carbon density estimates across a wide range of vegetation types in 2010 with quantified uncertainty. They do so for the globe at an unprecedented 300-meter spatial resolution and can be used to more holistically account for diverse vegetation carbon stocks in global analyses and greenhouse gas inventories.
On the origin and persistence of long-period pulsating X-ray sources

Mounting observational evidence and theoretical results suggest that most pulsing X-ray sources, including those with massive companions, are disk fed when significant mass transfer is taking place. Regardless of whether or not substantial braking has occurred before the onset of disk accretion, once it begins the resulting strong braking torque on fast rotators is sufficient to spin-down a neutron star from an initial period approx. 1 sec to a period approx. greater than 100 sec in a time short compared to the main-sequence lifetime of a massive companion and even, in many cases, in a time short compared to the star's lifetime as a bright X-ray source. It is argued that sources with longer periods have larger magnetic moments, or lower mean luminosities, or both, whereas sources with shorter periods have smaller magnetic moments, or higher mean luminosities, or both.
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?

Augmenting neural networks with skip connections, as introduced in the so-called ResNet architecture, surprised the community by enabling the training of networks of more than 1,000 layers with significant performance gains. This paper deciphers ResNet by analyzing the effect of skip connections, and puts forward new theoretical results on the advantages of identity skip connections in neural networks. We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective. We also show that, perhaps surprisingly, as more residual blocks are stacked, the norm-preservation of the network is enhanced. Our theoretical arguments are supported by extensive empirical evidence. Can we push for extra norm-preservation? We answer this question by proposing an efficient method to regularize the singular values of the convolution operator and making the ResNet’s transition layers extra norm-preserving. Our numerical investigations demonstrate that the learning dynamics and the classification performance of ResNet can be improved by making it even more norm preserving. Our results and the introduced modification for ResNet, referred to as Procrustes ResNets, can be used as a guide for training deeper networks and can also inspire new deeper architectures.
The Business Cycle, Financial Performance, and the Retirement of Capital Goods

Abstract The neoclassical investment literature assumes that capital is homogenous, lives forever, and has a constant depreciation rate. More recent theories of investment have shown that when there are distinct capital vintages with embodied technologies, depreciation and capital retirement become economic decisions and this raises important problems with existing empirical work. Direct testing of these issues, however, has been rare because of the lack of micro data. This paper uses new data on the service lives of individual capital goods in the airline industry to empirically examine the impact that economic factors have on capital retirement. The results strongly support the view that retirement is fundamentally an economic decision. Retirement is much more likely in recessions, when the cost of capital is low, or when a firm has good financial performance. Factor prices and industry regulation are also important. Since many of these factors also influence capital expenditures, the results imply that estimates from the conventional investment literature, such as the effect of the cost of capital or financial performance, may substantially overstate the case since their impact on net investment may be much more modest than their impact on gross investment. The results also have implications for the measurement of productivity. Journal of Economic Literature Classification Number: E22 8.
Adaptation Space: A Design Framework for Adaptive Web Services

Web service adaptation is an important feature for mission critical Web services. It is widely recognized that thrashing and crashes occur in system saturation for many statically adaptive resource management algorithms, including CPU, memory, and network congestion. Service adaptation supports alternative responses to saturation control, maintaining service and server system stability and progress, instead of thrashing. In this article, we present a design framework for developing adaptive Web services. The core of this framework is the adaptation space model, which is based on the concepts of adaptation space and adaptation case. An adaptation space is defined by a reference context and a partial order of adaptation cases. Each adaptation case describes a specific adaptation of a program or component of a Web service. We evaluate the adaptation space approach using Ginga, an adaptive query processing service for handling queries over multiple data sources with diverse capabilities across the Internet. Our experimental results show that Ginga query adaptation can achieve significant performance improvements (up to 40% of response time gain) for processing distributed queries over the Internet in the presence of end-to-end delays.
Solid-Phase Peptide Capture and Release for Bulk and Single-Molecule Proteomics.

The field of proteomics has expanded recently with more sensitive techniques for the bulk measurement of peptides as well as single-molecule techniques. One limiting factor for some of these method...
Chapter 1 – Getting Started

Publisher Summary Mathematica is a system for doing mathematics on computer. It combines symbolic manipulation, numerical mathematics, outstanding graphics, and a sophisticated programming language. Because of its versatility, Mathematica has established itself as the computer algebra system of choice for many computer users. It is the most powerful and most widely used program of this type. Among the over 100,000 users of Mathematica, 28% are engineers, 21% are computer scientists, 20% are physical scientists, 12% are mathematical scientists, and 12% are business, social, and life scientists. Two-thirds of the users are in industry and government with a small (8%) but growing number of student users. However, because of its special nature and sophistication, beginning users need to be aware of the special syntax required to make Mathematica perform in the way intended.
Symmetries of shamrocks IV: The self-complementary case

In this paper we enumerate the centrally symmetric lozenge tilings of a hexagon with a shamrock removed from its center. Our proof is based on a variant of Kuo's graphical condensation method in which only three of the four involved vertices are on the same face. As a special case, we obtain a new proof of the enumeration of the self-complementary plane partitions.
Prediction Analysis of Cross-Classifications†

Prediction analysis is an approach to the analysis of cross-classified, cross-sectional or longitudinal data. It is a method for predicting events based on specification of predicted relations among qualitative variables and includes techniques for both stating and evaluating those event predictions. This article presents the basic methodology employed in prediction analysis and highlights approaches developed by Hildebrand, Laing, and Rosenthal (6), and by von Eye, Brandtstadter & Rovine, (12, 13). The Hildebrand et al. approach involves measuring prediction success of a set of predictions via a proportionate reduction in error (PRE) measure, known as del. The von Eye et al. approach models the prediction hypothesis via nonstandard log-linear modeling and evaluates the extent to which the model adequately describes the data. Both approaches to prediction analysis provide a viable, valuable data analysis tool that researchers in the social and behavioral sciences can use in a wide range of research settings. Keywords: prediction analysis; prediction success; qualitative variables; cross-classified data; del; log-linear modeling
Chapter 6 – Microvascular Beds

In this chapter, some of the principles that regulate the flow of blood through the microcirculation are discussed. This chapter begins with the physiology of the capillary in comparison with the arteries and veins. Endothelial cell and smooth muscle cell physiology is also highlighted. Some possible mechanisms for information transfer through the microvascular network are discussed. The Stokes equation, which governs the flow through the microvasculature, and the mass transfer equations are developed. Changes in fluid properties, such as the hematocrit, and the viscosity within capillaries are described along with the causes for these changes. Plug flow and how this alters the pressure and velocity distribution within capillaries are described. The formulation for the accumulation of molecular species within a capillary is developed. Finally, two-phase flow and the interactions of blood cells with the capillary wall are described.
An exploration of the effects of pessimism and doubt on asset returns

The subjective distribution of growth rates of aggregate consumption is characterized by pessimism if it is first-order stochastically dominated by the objective distribution. Uniform pessimism is a leftward translation of the objective distribution of the logarithm of the growth rate. The subjective distribution is characterized by doubt if it is mean-preserving spread of the objective distribution. Pessimism and doubt both reduce the riskfree rate and thus can help resolve the riskfree rate puzzle. Uniform pessimism and doubt both increase the average equity premium and thus can help resolve the equity premium puzzle.
On the Poisson relation for compact Lie groups

Intuition drawn from quantum mechanics and geometric optics raises the following long-standing question: Can the length spectrum of a closed Riemannian manifold be recovered from its Laplace spectrum? By demonstrating that the Poisson relation is an equality for a generic bi-invariant metric on a compact Lie group, we establish that the length spectrum of a generic bi-invariant metric on a compact Lie group can be recovered from its Laplace spectrum. Furthermore, we exhibit a substantial collection $${\mathscr{G}}$$ of compact Lie groups—including those that are either tori, simple, simply connected, or products thereof—with the property that for each group $$U \in {\mathscr{G}}$$ the length spectrum of any bi-invariant metric g carried by U is encoded in the Laplace spectrum of g. The preceding statements are special cases of results concerning compact globally symmetric spaces for which the semi-simple part of the universal cover is split-rank. The manifolds considered herein join a short list of families of non-“bumpy” Riemannian manifolds for which the Poisson relation is known to be an equality.
Romanticizing the Poor Harms the Poor

A libertarian movement that emphasizes free markets to reduce poverty haS grown strong in recent years. It views the poor as "resilient and creative entrepreneurs and value-conscious consumers". This romanticized view of the poor is far from the truth and harms the poor in two ways. First, it results in too little emphasis on legal, regulatory, and social mechanisms to protect the poor who are vulnerable consumers. Second, it results in overemphasis on microcredit and under emphasis on fostering modern enterprises that would provide employment opportunities tm the poor. More importantly, the libertarian proposition grossly under-emphasizes the critical role and responsibility of the state for poverty reduction.
Small molecule inhibition of human cGAS reduces total cGAMP output and cytokine expression in cells

The cGAS-STING pathway is a major mechanism that mammalian cells utilize to detect cytoplasmic dsDNA from incoming viruses, bacteria, or self. CYCLIC GMP-AMP SYNTHASE (cGAS) is the sensor protein that directly binds dsDNAs. cGAS synthesizes cyclic GMP-AMP (cGAMP), which binds to the adaptor STIMULATOR OF INTERFERON GENES (STING), activating an INTERFERON REGULATORY FACTOR 3 (IRF3)-mediated immune response. Constitutive activation can result in interferonopathies such as Aicardi-Goutieres Syndrome (AGS) or other lupus-like autoimmune disorders. While inhibitors targeting mouse or human cGAS have been reported, the identification of a small molecule that targets both homologs of cGAS has been challenging. Here, we show that RU.521 is capable of potently and selectively inhibiting mouse and human cGAS in cell lines and human primary cells. This inhibitory activity requires the presence of cGAS, but it cannot suppress an immune response in cells activated by RNA, Toll-like receptor ligands, cGAMP, or recombinant interferon. Importantly, when RU.521 is applied to cells, the production of dsDNA-induced intracellular cGAMP is suppressed in a dose-dependent manner. Our work validates the use of RU.521 for probing DNA-induced innate immune responses and underscores its potential as an ideal scaffold towards pre-clinical development, given its potency against human and mouse cGAS.
Do endowments predict the location of production?: Evidence from national and international data

Abstract Examining the relationship between factor endowments and production patterns using data from Japanese prefectures and from OECD nations, we find evidence of substantial production indeterminacy. Regressions of outputs on endowments yield prediction errors six to 30 times larger for goods traded relatively freely than for non-traded goods. We argue that a compelling explanation for these results is the existence of more goods than factors in the presence of trade costs. If so, regressions of trade or output on endowments have weak theoretical foundations. Furthermore, since errors are largest in data sets where trade costs are small, we explain why the common methodology of imputing trade barriers from regression residuals has produced counterintuitive results.
Descents in $t$-sorted permutations

Let $s$ denote West's stack-sorting map. A permutation is called $t-\textit{sorted}$ if it is of the form $s^t(\mu)$ for some permutation $\mu$. We prove that the maximum number of descents that a $t$-sorted permutation of length $n$ can have is $\left\lfloor\frac{n-t}{2}\right\rfloor$. When $n$ and $t$ have the same parity and $t\geq 2$, we give a simple characterization of those $t$-sorted permutations in $S_n$ that attain this maximum. In particular, the number of such permutations is $(n-t-1)!!$.
Integrated production planning and scheduling optimization of multisite, multiproduct process industry

Abstract The current manufacturing environment for process industry has changed from a traditional single-site, single market to a more integrated global production mode where multiple sites are serving a global market. In this paper, the integrated planning and scheduling problem for the multisite, multiproduct batch plants is considered. The major challenge for addressing this problem is that the corresponding optimization problem becomes computationally intractable as the number of production sites, markets, and products increases in the supply chain network. To effectively deal with the increasing complexity, the block angular structure of the constraints matrix is exploited by relaxing the inventory constraints between adjoining time periods using the augmented Lagrangian decomposition method. To resolve the issues of non-separable cross-product terms in the augmented Lagrangian function, we apply diagonal approximation method. Several examples have been studied to demonstrate that the proposed approach yields significant computational savings compared to the full-scale integrated model.
Analysis of FGF20-regulated genes in organ of Corti progenitors by translating ribosome affinity purification.

Background Understanding the mechanisms that regulate hair cell (HC) differentiation in the organ of Corti (OC) is essential to designing genetic therapies for hearing loss due to HC loss or damage. We have previously identified Fibroblast Growth Factor 20 (FGF20) as having a key role in HC and supporting cell differentiation in the mouse OC. To investigate the genetic landscape regulated by FGF20 signaling in OC progenitors, we employ Translating Ribosome Affinity Purification combined with Next Generation RNA Sequencing (TRAPseq) in the Fgf20 lineage. Results We show that TRAPseq targeting OC progenitors effectively enriched for RNA from this rare cell population. TRAPseq identified differentially expressed genes (DEGs) downstream of FGF20, including Etv4, Etv5, Etv1, Dusp6, Hey1, Hey2, Heyl, Tectb, Fat3, Cpxm2, Sall1, Sall3, and cell cycle regulators such as Cdc20. Analysis of Cdc20 conditional-null mice identified decreased cochlea length, while analysis of Sall1-null and Sall1-ΔZn2-10 mice, which harbor a mutation that causes Townes-Brocks syndrome, identified a decrease in outer hair cell number. Conclusions We present two datasets: genes with enriched expression in OC progenitors, and DEGs downstream of FGF20 in the embryonic day 14.5 cochlea. We validate select DEGs via in situ hybridization and in vivo functional studies in mice.
Regularity of homogenized boundary data in periodic homogenization of elliptic systems

This paper is concerned with periodic homogenization of second-order elliptic systems in divergence form with oscillating Dirichlet data or Neumann data of first order. We prove that the homogenized boundary data belong to $W^{1, p}$ for any $1<p<\infty$. In particular, this implies that the boundary layer tails are Holder continuous of order $\alpha$ for any $\alpha \in (0,1)$.
Contextual Embeddings: When Are They Worth It?

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline---random word embeddings---focusing on the impact of the training set size and the linguistic properties of the task. Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10% accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.
Parametric mode regression for bounded responses.

We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supporting Information that contain technical details and additional simulation and data analysis results are available online.
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network (CNN) unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the CNN unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large inter-annual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate high-aridity basins with one-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.