cssi/SciSciGPT-SciSciNet huggingface dataset:
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.
PARQR: Augmenting the Piazza Online Forum to Better Support Degree Seeking Online Masters Students

We introduce PARQR, a tool for online education forums that reduces duplicate posts by 40% in a degree seeking online masters program at a top university. Instead of performing a standard keyword search, PARQR monitors questions as students compose them and continuously suggests relevant posts. In testing, PARQR correctly recommends a relevant post, if one exists, 73.5% of the time. We discuss PARQR's design, initial experimental results comparing different semesters with and without PARQR, and interviews we conducted with teaching instructors regarding their experience with PARQR.
DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout

Motivation Exciting new opportunities have arisen to solve the protein contact prediction problem from the progress in neural networks and the availability of a large number of homologous sequences through high-throughput sequencing. In this work, we study how deep convolutional neural networks (ConvNets) may be best designed and developed to solve this long-standing problem. Results With publicly available datasets, we designed and trained various ConvNet architectures. We tested several recent deep learning techniques including wide residual networks, dropouts and dilated convolutions. We studied the improvements in the precision of medium-range and long-range contacts, and compared the performance of our best architectures with the ones used in existing state-of-the-art methods. The proposed ConvNet architectures predict contacts with significantly more precision than the architectures used in several state-of-the-art methods. When trained using the DeepCov dataset consisting of 3456 proteins and tested on PSICOV dataset of 150 proteins, our architectures achieve up to 15% higher precision when L/2 long-range contacts are evaluated. Similarly, when trained using the DNCON2 dataset consisting of 1426 proteins and tested on 84 protein domains in the CASP12 dataset, our single network achieves 4.8% higher precision than the ensembled DNCON2 method when top L long-range contacts are evaluated. Availability and implementation DEEPCON is available at https://github.com/badriadhikari/DEEPCON/.
Using Delay Tolerant Networks as a Backbone for Low-Cost Smart Cities

Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of Internet-of-things (IoT) devices to monitor and manage environmental conditions and infrastructure. However, smart city projects can be extremely expensive to deploy and manage. A significant portion of the expense is a result of providing Internet connectivity via 5G or WiFi to IoT devices. This paper proposes the use of delay tolerant networks (DTNs) as a backbone for smart city communication; enabling developing communities to become smart cities at a fraction of the cost. A model is introduced to aid policy makers in designing and evaluating the expected performance of such networks. Preliminary results are presented based on a public transit network data-set from Chapel Hill, North Carolina. Finally, innovative ways of improving network performance in a low-cost smart city is discussed.
Robust and Efficient Power Flow Convergence with G-min Stepping Homotopy Method

Recent advances have shown that the circuit simulation algorithms that allow for solving highly nonlinear circuits of over one billion variables can be applicable to power system simulation and optimization problems through the use of an equivalent circuit formulation. It was demonstrated that large-scale (80k+ buses) power flow simulations can be robustly solved, independent of the initial starting point. In this paper, we extend the electronic circuit-based G-min stepping homotopy method to power flow simulations. Preliminary results indicate that the proposed algorithm results in significantly better simulation runtime performance when compared to existing homotopy methods.
Twisty Takens: a geometric characterization of good observations on dense trajectories

In nonlinear time series analysis and dynamical systems theory, Takens’ embedding theorem states that the sliding window embedding of a generic observation along trajectories in a state space, recovers the region traversed by the dynamics. This can be used, for instance, to show that sliding window embeddings of periodic signals recover topological loops, and that sliding window embeddings of quasiperiodic signals recover high-dimensional torii. However, in spite of these motivating examples, Takens’ theorem does not in general prescribe how to choose such an observation function given particular dynamics in a state space. In this work, we state conditions on observation functions defined on compact Riemannian manifolds, that lead to successful reconstructions for particular dynamics. We apply our theory and construct families of time series whose sliding window embeddings trace tori, Klein bottles, spheres, and projective planes. This greatly enriches the set of examples of time series known to concentrate on various shapes via sliding window embeddings, and will hopefully help other researchers in identifying them in naturally occurring phenomena. We also present numerical experiments showing how to recover low dimensional representations of the underlying dynamics on state space, by using the persistent cohomology of sliding window embeddings and Eilenberg–MacLane (i.e. circular and real projective) coordinates.
A mathematical model of calcium dynamics: Obesity and mitochondria-associated ER membranes.

Multiple cellular organelles tightly orchestrate intracellular calcium (Ca2+) dynamics to regulate cellular activities and maintain homeostasis. The interplay between the endoplasmic reticulum (ER), a major store of intracellular Ca2+, and mitochondria, an important source of adenosine triphosphate (ATP), has been the subject of much research, as their dysfunction has been linked with metabolic diseases. Interestingly, throughout the cell’s cytosolic domain, these two organelles share common microdomains called mitochondria-associated ER membranes (MAMs), where their membranes are in close apposition. The role of MAMs is critical for intracellular Ca2+ dynamics as they provide hubs for direct Ca2+ exchange between the organelles. A recent experimental study reported correlation between obesity and MAM formation in mouse liver cells, and obesity-related cellular changes that are closely associated with the regulation of Ca2+ dynamics. We constructed a mathematical model to study the effects of MAM Ca2+ dynamics on global Ca2+ activities. Through a series of model simulations, we investigated cellular mechanisms underlying the altered Ca2+ dynamics in the cells under obesity. We predict that, as the dosage of stimulus gradually increases, liver cells from obese mice will reach the state of saturated cytosolic Ca2+ concentration at a lower stimulus concentration, compared to cells from healthy mice.
The time-varying diversifiability of corporate foreign exchange exposure

Abstract Estimating comovement measures for a large set of bilateral foreign exchange (FX) rates, I explore the relation between firm-level FX exposure and its time-varying diversifiability. For a sample of U.S. firms, the magnitude of FX exposure appears to increase during periods of low currency risk diversifiability. Additional results suggest that the introduction of the euro exacerbated the effect of diversifiability on developed market currency exposure. Moreover, the low diversifiability of emerging market currencies seems to have a stronger effect on FX exposure than the low diversifiability of developed market currencies.
On the stability of the unsteady boundary layer on a cylinder oscillating transversely in a viscous fluid

The stability of the two-dimensional flow induced by the transverse oscillation of a cylinder in a viscous fluid is investigated in both the linear and weakly nonlinear regimes. The major assumption that is made to simplify the problem is that the oscillation frequency is large, in which case an unsteady boundary layer is set up on the cylinder. The basic flow induced by the motion of the cylinder depends on two spatial variables, and is periodic in time. The stability analysis of this flow to axially periodic disturbances therefore leads to a partial differential system dependent on three variables. In the high-frequency limit the linear stability problem can be reduced to a system dependent only on a radial variable and time. Furthermore, the coefficients of the differential operators in this system are periodic in time, so that Floquet theory can be used to reduce this system further to a coupled infinite system of ordinary differential equations together with uncoupled homogeneous boundary conditions. The eigenvalues of this system are found numerically and predict instability entirely consistent with the experiments with circular cylinders performed by Honji (1981). Results are given for cylinders of elliptic cross-section, and it is found that for any given eccentricity the most dangerous configuration is when the cylinder oscillates parallel to its minor axis. Some discussion of nonlinear effects is also given, and for the circular cylinder it is shown that the steady-streaming boundary layer of the basic flow is significantly altered by the instability.
Expanding the genetic toolkit of Tribolium castaneum.

The red flour beetle, Tribolium castaneum, is an important model insect and agricultural pest. However, many standard genetic tools are lacking or underdeveloped in this system. Here, we present a set of new reagents to augment existing Tribolium genetic tools. We demonstrate a new GAL4 driver line that employs the promoter of a ribosomal protein gene to drive expression of a UAS responder in the fat body. We also present a novel dual fluorescent reporter that labels cell membranes and nuclei with different fluorophores for the analysis of cellular morphology. This approach also demonstrates the functionality of the viral T2A peptide for bicistronic gene expression in Tribolium. To facilitate classical genetic analysis, we created lines with visible genetic markers by CRISPR-mediated disruption of the yellow and ebony body color loci with a cassette carrying an attP site, enabling future φC31-mediated integration. Together, the reagents presented here will facilitate more robust genetic analysis in Tribolium and serve as a blueprint for the further development of this powerful model’s genetic toolkit.
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled text. However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on text from the target domain. We test this approach on sequence labeling in two challenging domains: Early Modern English and Twitter. Both domains differ substantially from existing pretraining corpora, and domain-adaptive fine-tuning yields substantial improvements over strong BERT baselines, with particularly impressive results on out-of-vocabulary words. We conclude that domain-adaptive fine-tuning offers a simple and effective approach for the unsupervised adaptation of sequence labeling to difficult new domains.
On the rock-physics basis for seismic hydrocarbon detection

ABSTRACTOne of the primary fluid indicators for direct hydrocarbon detection in sandstones using seismic reflectivity is the difference between the saturated-rock P-wave impedance and the rock-fram...
Exploring Methods for the Automatic Detection of Errors in Manual Transcription.

Quality of data plays an important role in most deep learning tasks. In the speech community, transcription of speech recording is indispensable. Since the transcription is usually generated artificially, automatically finding errors in manual transcriptions not only saves time and labors but benefits the performance of tasks that need the training process. Inspired by the success of hybrid automatic speech recognition using both language model and acoustic model, two approaches of automatic error detection in the transcriptions have been explored in this work. Previous study using a biased language model approach, relying on a strong transcription-dependent language model, has been reviewed. In this work, we propose a novel acoustic model based approach, focusing on the phonetic sequence of speech. Both methods have been evaluated on a completely real dataset, which was originally transcribed with errors and strictly corrected manually afterwards.
Conformity bias in the cultural transmission of music sampling traditions

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that fre...
Orbit space curvature as a source of mass in quantum gauge theory

It has long been realized that the natural orbit space for non-abelian Yang-Mills dynamics is a positively curved (infinite dimensional) Riemannian manifold. Expanding on this result I.M. Singer proposed that strict positivity of the corresponding Ricci tensor (computable through zeta function regularization) could play a fundamental role in establishing that the associated Schroedinger operator admits a spectral gap. His argument was based on representing the (regularized) kinetic term in the Schroedinger operator as a Laplace-Beltrami operator on this positively curved orbit space. We revisit Singer's proposal and show how, when the contribution of the Yang-Mills potential energy is taken into account, the role of the original orbit space Ricci tensor is instead played by a Bakry-Emery Ricci tensor computable from the ground state wave functional of the quantum theory. We next review our ongoing Euclidean-signature-semi-classical program for deriving asymptotic expansions for such wave functionals and discuss how, by keeping the dynamical nonlinearities and non-abelian gauge invariances intact at each level of the analysis, our approach surpasses that of conventional perturbation theory for the generation of approximate wave functionals. Though our main focus is on Yang-Mills theory we derive the orbit space curvature for scalar electrodynamics and prove that, whereas the Maxwell factor remains flat, the interaction naturally induces positive curvature in the (charged) scalar factor of the resulting orbit space. This has led us to the conjecture that such orbit space curvature effects could furnish a source of mass for ordinary Klein-Gordon type fields provided the latter are (minimally) coupled to gauge fields, even in the abelian case. Finally we discuss the potential applicability of our Euclidean-signature program to the Wheeler-DeWitt equation of canonical quantum gravity.
Model of pattern formation in marsh ecosystems with nonlocal interactions

Smooth cordgrass Spartina alterniflora is a grass species commonly found in tidal marshes. It is an ecosystem engineer, capable of modifying the structure of its surrounding environment through various feedbacks. The scale-dependent feedback between marsh grass and sediment volume is particularly of interest. Locally, the marsh vegetation attenuates hydrodynamic energy, enhancing sediment accretion and promoting further vegetation growth. In turn, the diverted water flow promotes the formation of erosion troughs over longer distances. This scale-dependent feedback may explain the characteristic spatially varying marsh shoreline, commonly observed in nature. We propose a mathematical framework to model grass–sediment dynamics as a system of reaction–diffusion equations with an additional nonlocal term quantifying the short-range positive and long-range negative grass–sediment interactions. We use a Mexican-hat kernel function to model this scale-dependent feedback. We perform a steady state biharmonic approximation of our system and derive conditions for the emergence of spatial patterns, corresponding to a spatially varying marsh shoreline. We find that the emergence of such patterns depends on the spatial scale and strength of the scale-dependent feedback, specified by the width and amplitude of the Mexican-hat kernel function.
Blastula stage specification of avian neural crest

Cell fate specification defines the earliest steps towards a distinct cell lineage. Neural crest, a multipotent stem cell population, is thought to be specified from the ectoderm, but its varied contributions defy canons of segregation potential and challenges its embryonic origin. Aiming to resolve this conflict, we have assayed the earliest specification of neural crest using blastula stage chick embryos. Specification assays on isolated chick epiblast explants identify an intermediate region specified towards the neural crest cell fate. Furthermore, low density culture suggests that the specification of intermediate cells towards the neural crest lineage is independent of contact mediated induction and Wnt-ligand induced signaling, but is, however, dependent on transcriptional activity of β-catenin. Finally, we have validated the regional identity of the intermediate region towards the neural crest cell fate using fate map studies. Our results suggest a model of neural crest specification within a restricted epiblast region in blastula stage chick embryos.
Toward a methodology for studying leadership-as-practice:

This article is intended as a conceptual and practical foundation for those who wish to conduct research in the area of leadership-as-practice. Rather than offer a single methodology for studying h...
Type decomposition in NIP theories

We prove that any type in an NIP theory can be decomposed into a stable part (a generically stable partial type) and a distal-like quotient.
The Effect of Hops (Humulus lupulus L.) Extract Supplementation on Weight Gain, Adiposity and Intestinal Function in Ovariectomized Mice

Estrogen decline during menopause is associated with altered metabolism, weight gain and increased risk of cardiometabolic diseases. The gut microbiota also plays a role in the development of cardiometabolic dysfunction and is also subject to changes associated with age-related hormone changes. Phytoestrogens are plant-based estrogen mimics that have gained popularity as dietary supplements for the treatment or prevention of menopause-related symptoms. These compounds have the potential to both modulate and be metabolized by the gut microbiota. Hops (Humulus lupulus L.) contain potent phytoestrogen precursors, which rely on microbial biotransformation in the gut to estrogenic forms. We supplemented ovariectomized (OVX) or sham-operated (SHAM) C57BL/6 mice, with oral estradiol (E2), a flavonoid-rich extract from hops, or a placebo carrier oil, to observe effects on adiposity, inflammation, and gut bacteria composition. Hops extract (HE) and E2 protected against increased visceral adiposity and liver triglyceride accumulation in OVX animals. Surprisingly, we found no evidence of OVX having a significant impact on the overall gut bacterial community structure. We did find differences in the abundance of Akkermansia muciniphila, which was lower with HE treatment in the SHAM group relative to OVX E2 treatment and to placebo in the SHAM group.
Multisketches: Practical Secure Sketches Using Off-the-Shelf Biometric Matching Algorithms

Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful "fuzzy" cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics --- e.g., multiple fingerprints --- of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints --- fingerprints of ten fingers --- called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database.
Options valuation and calibration for leveraged exchange-traded funds with Heston–Nandi and inverse Gaussian GARCH models

Leveraged exchange-traded funds (LETF) are newly introduced ETFs that have become increasingly popular. It closely tracks the value of an underlying index while allowing for additional leverage. In...
Moment subset sums over finite fields

Abstract The k-subset sum problem over finite fields is a classical NP-complete problem. Motivated by coding theory applications, a more complex problem is the higher m-th moment k-subset sum problem over finite fields. We show that there is a deterministic polynomial time algorithm for the m-th moment k-subset sum problem over finite fields for each fixed m when the evaluation set is the image set of a monomial or Dickson polynomial of any degree n. In the classical case m = 1 , this recovers previous results of Nguyen-Wang (the case m = 1 , p > 2 ) [22] and the results of Choe-Choe (the case m = 1 , p = 2 ) [3] .
Protein kinase A activity is regulated by actomyosin contractility during cell migration and is required for durotaxis

Dynamic subcellular regulation of protein kinase A (PKA) activity is important for the motile behavior of many cell types, yet the mechanisms governing PKA activity during cell migration remain largely unknown. The motility of SKOV-3 epithelial ovarian cancer (EOC) cells has been shown to be dependent both on localized PKA activity and, more recently, on mechanical reciprocity between cellular tension and extracellular matrix rigidity. Here, we investigated the possibility that PKA is regulated by mechanical signaling during migration. We find that localized PKA activity in migrating cells rapidly decreases upon inhibition of actomyosin contractility (specifically, of myosin ATPase, Rho kinase, or myosin light-chain kinase activity). Moreover, PKA activity is spatially and temporally correlated with cellular traction forces in migrating cells. Additionally, PKA is rapidly and locally activated by mechanical stretch in an actomyosin contractility-dependent manner. Finally, inhibition of PKA activity inhibits mechanically guided migration, also known as durotaxis. These observations establish PKA as a locally regulated effector of cellular mechanotransduction and as a regulator of mechanically guided cell migration.
The Effectiveness of White-Collar Crime Enforcement: Evidence from the War on Terror

This paper analyzes the impact of changes in regulatory priorities and resource allocation on criminal enforcement of white‐collar criminal activities. Using the 9/11 terrorist attacks as a shock to the FBI's priorities and allocation of investigative resources, as well as variation in the Muslim population in the United States, I examine whether prioritization of counterterrorism investigations after 9/11 is associated with weaker enforcement of laws targeting white‐collar crime. I then use a difference‐in‐differences estimation to study the magnitude of any increase in white‐collar crime resulting from reduced oversight. I find a significantly greater reduction in white‐collar criminal cases referred by FBI field offices that shifted more of their investigative focus away from white‐collar crime to counterterrorism. Further, geographic areas in the jurisdictions of FBI field offices with greater shifts in attention from white‐collar crime to counterterrorism experienced greater increases in wire fraud, illegal insider‐trading activities, and fraud within financial institutions.
VIX futures term structure and the expectations hypothesis

Tests of the expectations hypothesis reveal that the slope of the VIX futures term structure predicts the direction but not the magnitude of the evolution of the short-end of the curve, but predict...
Hydrogen Isotope Separation within the Metal–Organic Framework Cu(I)-MFU-4l

Hydrogen isotope separation within the metal–organic framework material known as Cu(I)-MFU-4l is investigated using a combination of breakthrough analysis, equilibrium measurements, and infrared sp...
Assessment of Bias in Estimates of Sexual Network Degree using Prospective Cohort Data.

Background Sexual network degree, a count of ongoing partnerships, plays a critical role in the transmission dynamics of human immunodeficiency virus and other sexually transmitted infections. Researchers often quantify degree using self-reported cross-sectional data on the day of survey, which may result in bias because of uncertainty about future sexual activity. Methods We evaluated the bias of a cross-sectional degree measure with a prospective cohort study of men who have sex with men (MSM). At baseline, we asked men about whether recent sexual partnerships were ongoing. We confirmed the true, ongoing status of those partnerships at baseline at follow-up. With logistic regression, we estimated the partnership-level predictors of baseline measure accuracy. With Poisson regression, we estimated the longitudinally confirmed degree as a function of baseline predicted degree. Results Across partnership types, the baseline ongoing status measure was 70% accurate, with higher negative predictive value (91%) than positive predictive value (39%). Partnership exclusivity and racial pairing were associated with higher accuracy. Baseline degree generally overestimated confirmed degree. Bias, or number of ongoing partners different than predicted at baseline, was -0.28 overall, ranging from -1.91 to -0.41 for MSM with any ongoing partnerships at baseline. Comparing MSM of the same baseline degree, the level of bias was stronger for black compared with white MSM, and for younger compared with older MSM. Conclusions Research studies may overestimate degree when it is quantified cross-sectionally. Adjustment and structured sensitivity analyses may account for bias in studies of human immunodeficiency virus or sexually transmitted infection prevention interventions.
Effects of Environmental Variables on some Physiological Responses of Microtus montanus Under Natural Conditions

Cyclic fluctuations in the population density of microtine rodents have been known since antiquity. However, factors responsible for this phenomenon are not known. The objectives of this long term study are essentially fourfold. First, to characterize the environmental variables that might affect Microtus in different seasons of the year. Second, to record the growth, maturation and reproductive activity of Microtus montanus under natural conditions. Third, to determine the maturational, as well as, seasonal pelage changes of these rodents. Fourth, the data resulting from the execution of the first three objectives would be correlated in an attempt to determine the causes underlying the multiannual fluctuations in population density of these microtine rodents.
Information in the Reflected-light Spectra of Widely Separated Giant Exoplanets

Giant exoplanets located >1 au away from their parent stars have atmospheric environments cold enough for water or ammonia clouds. We have developed a new equilibrium cloud and reflected-light spectrum model, ExoREL, for widely separated giant exoplanets. The model includes the dissolution of ammonia in liquid-water cloud droplets, an effect studied for the first time for exoplanets. While preserving the causal relationship between temperature and cloud condensation, ExoREL is simple and fast to enable efficient exploration of parameter space. Using the model, we find that the mixing ratio of methane and the cloud top pressure of a giant exoplanet can be uniquely determined from a single observation of its reflected-light spectrum at wavelengths less than 1 μm if it has a cloud deck deeper than ~0.3 bar. This measurement is enabled by the weak and strong bands of methane and requires a signal-to-noise ratio of 20. The cloud pressure, once derived, provides information about the internal heat flux of the planet. Importantly, we find that for a low, Uranus-like internal heat flux, the planet can have a deep liquid-water cloud, which will sequester ammonia and prevent the formation of the ammonia cloud that would otherwise be the uppermost cloud layer. This newly identified phenomenon causes a strong sensitivity of the cloud top pressure to the internal heat flux. Reflected-light spectroscopy from future direct-imaging missions should therefore not only measure the atmospheric abundances but also characterize the thermal evolution of giant exoplanets.
Sustained Coevolution in a Stochastic Model of Cancer–Immune Interaction

The dynamic interactions between an evolving malignancy and the adaptive immune system generate diverse evolutionary trajectories that ultimately result in tumor clearance or immune escape. Here, we create a simple mathematical model coupling T-cell recognition with an evolving cancer population that may randomly produce evasive subclones, imparting transient protection against the effector T cells. T-cell turnover declines and evasion rates together explained differences in early incidence data across almost all cancer types. Fitting the model to TRACERx evolutionary data argued in favor of substantial and sustained immune pressure exerted upon a developing tumor, suggesting that clinically observed incidence is a small proportion of all cancer initiation events. This dynamical model promises to increase our quantitative understanding of many immune escape contexts, including cancer progression and intracellular pathogenic infections. SIGNIFICANCE: The early cancer-immune interaction sculpts intratumor heterogeneity through the selection of immune-evasive clones. This study provides a mathematical framework for investigating the coevolution between an immune-evasive cancer population and the adaptive immune system.
Nonparametric tests for transition probabilities in nonhomogeneous Markov processes.

This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time nonhomogeneous Markov process with a...
Animal movement models with mechanistic selection functions

Abstract A suite of statistical methods are used to study animal movement. Most of these methods treat animal telemetry data in one of three ways: as discrete processes, as continuous processes, or as point processes. We briefly review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statistically treat animal telemetry data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specifications that provide inference about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specifications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial differential equation derived from first principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the effects of spatially explicit environmental conditions on movement behavior of this species.
Novel design integration for advanced nuclear heat-pipe systems

Abstract A new design integrating heat-pipes into a nuclear cooling system is presented. The heat-pipes are presented as the primary mode of heat transfer. Analyzing the prevailing limits to determine suitability for predicting the performance of the heat-pipe concept. When the limits are determined for the design integration, steady-state behavior needs to be quantified. A model that accounts for 3D behavior is evaluated for use. Using the limits to evaluate the design integration, with sodium, the operating regime would still remain below the predicted limits. With potassium the operating regime would exceed the capillary limit. This is caused by the increase in pressure drop. With the 3D model, a validation shows that conduction can give very good results for both transient and steady-state behavior for sodium. It shows that water has poor transient prediction but accurately predicts the steady-state behavior. Both solutions were close to the reported experimental results for steady-state.
FastCal: Robust Online Self-calibration for Robotic Systems

We propose a solution for sensor extrinsic self-calibration with low time complexity, competitive accuracy and graceful handling of often avoided corner cases: drift in calibration parameters and unobservable directions in the parameter space. It consists of three main parts: (1) information-theoretic based segment selection for constant-time estimation; (2) observability-aware parameter update through a rank-revealing decomposition of the Fischer information matrix; (3) drift-correcting self-calibration through the time-decay of segments. At the core of our FastCal algorithm is the loosely-coupled formulation for sensor extrinsics calibration and efficient selection of measurements. FastCal runs up to an order of magnitude faster than similar self-calibration algorithms (camera-to-camera extrinsics, excluding feature-matching and image pre-processing on all comparisons.), making FastCal ideal for integration into existing, resource-constrained, robotics systems.
A cytochemical study of the feulgen nucleal reaction

1. The base, sugar, phosphoric acid, and histone fractions of desoxy pentose nucleoproteins of cartilage nuclei have been followed throughout the standard Feulgen hydrolysis. 2. The indications are that at an optimal hydrolysis, one half the total base content has been removed; presumably the purines. During post-optimal hydrolysis the whole remaining thymic acid molecule plus the histone fraction of the protein has been removed from the nuclei. 3. The possibility of using the Feulgen reaction for quantitative estimation of desoxypentose nucleic acid is briefly considered, and it is concluded that, under properly controlled conditions and with adequate corrections, this method is comparable in accuracy with the photometric determination of nucleic acid in a cytological preparation by the ultra-violet absorption method.
How to make a giant bubble

The world's largest free-floating soap bubbles can encompass a small automobile and span nearly 100 square meters, despite being only microns thick. Experiments show that long, polydisperse polymer chains provide extensional properties during bubble formation and reduce gravitational drainage at high concentrations. However, too many polymers inhibit bubble formation, leading to an ideal concentration well below the characteristic overlap concentration for polymers in solution.
Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation

Assessing dietary intake accurately remains an open and challenging research problem. In recent years, image-based approaches have been developed to automatically estimate food intake by capturing eat occasions with mobile devices and wearable cameras. To build a reliable machine-learning models that can automatically map pixels to calories, successful image-based systems need large collections of food images with high quality groundtruth labels to improve the learned models. In this paper, we introduce a semi-automatic system for online food image collection and annotation. Our system consists of a web crawler, an automatic food detection method and a web-based crowdsoucing tool. The web crawler is used to download large sets of online food images based on the given food labels. Since not all retrieved images contain foods, we introduce an automatic food detection method to remove irrelevant images. We designed a web-based crowdsourcing tool to assist the crowd or human annotators to locate and label all the foods in the images. The proposed semi-automatic online food image collection system can be used to build large food image datasets with groundtruth labels efficiently from scratch.
Low-Resource Domain Adaptation for Speaker Recognition Using Cycle-Gans

Current speaker recognition technology provides great performance with the x-vector approach. However, performance decreases when the evaluation domain is different from the training domain, an issue usually addressed with domain adaptation approaches. Recently, unsupervised domain adaptation using cycle-consistent Generative Adversarial Networks (CycleGAN) has received a lot of attention. Cycle-GAN learn mappings between features of two domains given non-parallel data. We investigate their effectiveness in low resource scenario i.e. when limited amount of target domain data is available for adaptation, a case unexplored in previous works. We experiment with two adaptation tasks: microphone to telephone and a novel reverberant to clean adaptation with the end goal of improving speaker recognition performance. Number of speakers present in source and target domains are 7000 and 191 respectively. By adding noise to the target domain during CycleGAN training, we were able to achieve better performance compared to the adaptation system whose CycleGAN was trained on a larger target data. On reverberant to clean adaptation task, our models improved EER by 18.3% relative on VOiCES dataset compared to a system trained on clean data. They also slightly improved over the state-of-the-art Weighted Prediction Error (WPE) de-reverberation algorithm.
Integrating NVIDIA Deep Learning Accelerator (NVDLA) with RISC-V SoC on FireSim

NVDLA is an open-source deep neural network (DNN) accelerator which has received a lot of attention by the community since its introduction by Nvidia. It is a full-featured hardware IP and can serve as a good reference for conducting research and development of SoCs with integrated accelerators. However, an expensive FPGA board is required to do experiments with this IP in a real SoC. Moreover, since NVDLA is clocked at a lower frequency on an FPGA, it would be hard to do accurate performance analysis with such a setup. To overcome these limitations, we integrate NVDLA into a real RISC-V SoC on the Amazon cloud FPGA using FireSim, a cycle-exact FPGA-accelerated simulator. We then evaluate the performance of NVDLA by running YOLOv3 object-detection algorithm. Our results show that NVDLA can sustain 7.5 fps when running YOLOv3. We further analyze the performance by showing that sharing the last-level cache with NVDLA can result in up to 1.56x speedup. We then identify that sharing the memory system with the accelerator can result in unpredictable execution time for the real-time tasks running on this platform. We believe this is an important issue that must be addressed in order for on-chip DNN accelerators to be incorporated in real-time embedded systems.
Vibration can enhance stick-slip behavior for granular friction

We experimentally study the frictional behavior of a two-dimensional slider pulled slowly over a granular substrate comprised of photoelastic disks. The slider is vibrated at frequencies ranging from 0 to 30 Hz in a direction parallel to sliding. The applied vibrations have constant peak acceleration, which results in constant average friction levels. Surprisingly, we find that stick-slip behavior, where stress slowly builds up and is released in intermittent slips, is enhanced as the frequency of vibration is increased. Our results suggest that increasing the frequency of vibration may help to combine many smaller rearrangements into fewer, but larger, avalanche-like slips, a mechanism unique to granular systems. We also examine the manner in which the self-affine character of the force curves evolves with frequency, and we find additional support for this interpretation.
Conditional antibody expression to avoid central B cell deletion in humanized HIV-1 vaccine mouse models

HIV-1 vaccine development aims to elicit broadly neutralizing antibodies (bnAbs) against diverse viral strains. In some HIV-1-infected individuals, bnAbs evolved from precursor antibodies through affinity maturation. To induce bnAbs, a vaccine must mediate a similar antibody maturation process. One way to test a vaccine is to immunize mouse models that express human bnAb precursors and assess whether the vaccine can convert precursor antibodies into bnAbs. A major problem with such mouse models is that bnAb expression often hinders B cell development. Such developmental blocks may be attributed to the unusual properties of bnAb variable regions, such as poly-reactivity and long antigen-binding loops, which are usually under negative selection during primary B cell development. To address this problem, we devised a method to circumvent such B cell developmental blocks by expressing bnAbs conditionally in mature B cells. We validated this method by expressing the unmutated common ancestor (UCA) of the human VRC26 bnAb in transgenic mice. Constitutive expression of the VRC26UCA led to developmental arrest of B cell progenitors in bone marrow; poly-reactivity of the VRC26UCA and poor pairing of the VRC26UCA heavy chain with the mouse surrogate light chain may contribute to this phenotype. The conditional expression strategy bypassed the impediment to VRC26UCA B cell development, enabling the expression of VRC26UCA in mature B cells. This approach should be generally applicable for expressing other bnAbs that are under negative selection during B cell development.
Complex Tori, Theta Groups and Their Jordan Properties

We prove that an analog of Jordan’s theorem on finite subgroups of general linear groups does not hold for the group of bimeromorphic automorphisms of a product of the complex projective line and a complex torus of positive algebraic dimension.
Rate-Constrained Shaping Codes for Structured Sources

Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor – the ratio of the expected codeword length to the length of the input source word – and the minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.
Exchange Rate Dynamics with Sticky Prices: The Deutsch Mark, 1974-1982

This article estimates simultaneously dynamic equations for the Deutsche mark/dollar exchange rate and the German wholesale price index. These emerge from a model in which German prices are sticky. The stickiness is due to costs of adjusting prices of the form posited by Rotemberg (1982a,b). The main results of the empirical analysis are first that the version of the model in which prices are perfectly flexible is rejected and, second that, in spite of the substantial price stickiness, we find that the nominal exchange rate undershoots in response to monetary innovations like those that appear to be typical in Germany.
International R&D tournaments and Industrial Policy

This paper provides a unified approach to study the influence of uncertainty and spillovers on the direction of R&D policy when firms engage in international R&D competition. When the reward to the winner is exogenously given, it is shown that whether a government will tax or subsidize its firm is sensitive to the type of uncertainty that characterizes the R&D process. When the reward to the winner is endogenously determined by R&D spending, the direction of optimal policy is not only sensitive to the type of uncertainty, but also sensitive to the degree of spillovers.