cssi/SciSciGPT-SciSciNet huggingface dataset:
Efficient living subject localization and weak vital-sign signal enhancement using impulse radio based UWB radar

In recent years, with the help of Ultra-wideband (UWB) radar, contactless measurement of vital signs become possible. This type of radar has sufficient range accuracy for micro-motion such as chest displacement or belly displacement. However, ambient clutter is strong in some scenarios such as a vehicle cabin so that a vital sign of living subject could be obscured and hard to extract. In addition, for multi-person vital signs detection, the sign of people sitting further away from the radar could become weaker due to the strong signal reflected by people in front. This paper describes an efficient way of a vital sign signal localization and a cross-correlation based method to enhance vital signs. The localization method proposed a threshold-based searching method to reduce searching range and save computational power for localization computation. The purpose of vital signs enhancement is 1) to make the weak vital sign signal observable and 2) to increase the signal-to-noise ratio (SNR) of a living subject sitting in at some distance for better localization and vital sign signal extraction accuracy. The enhancement algorithm is to cross-correlate the adjacent bins of the original signal for vital sign signal amplification. Tests are made with multiple people sitting in indoor space with strong reflected material around to simulate vehicle cabin environment. This method has been proved effective for vital sign enhancement in which the SNR increased by more than 40%. In addition, the speed of localization has increased by more than 20% for the two executed test scenarios.
Histology Personal Trainer: Identifying Tissue Types Using Critical Thinking and Metacognition Prompts.

Metacognitive reflections embedded in course activities allow students to evaluate their learning immediately following the activities themselves, while providing instructors the opportunity to revise and optimize instruction to target misconceptions. This activity, “Histology Personal Trainer” helps students not only identify major tissue types, but reflect on how they identified them. This activity aims to improve student performance by exercising the students’ observation and comparative skills in combination with a self-reflection activity that helps to identify successful and unsuccessful identification strategies. By the end of the activity, students should be able to adapt their approach to obtain more accurate answers. This activity was used in an anatomy and physiology course, but could be modified for use in various STEM classes.
Geometric Local Variance Gamma Model

This article describes another extension of the local variance gamma model originally proposed by Carr in 2008 and then further elaborated by Carr and Nadtochiy in 2017 and Carr and Itkin in 2018. As compared with the latest version of the model developed by Carr and Itkin and called the “expanded local variance gamma” (ELVG) model, two innovations are provided in this article. First, in all previous articles the model was constructed on the basis of a gamma time-changed arithmetic Brownian motion: with no drift in Carr and Nadtochiy, with drift in Carr and Itkin, and with the local variance a function of the spot level only. In contrast, this article develops a geometric version of this model with drift. Second, in Carr and Nadtochiy the model was calibrated to option smiles assuming that the local variance is a piecewise constant function of strike, while in Carr and Itkin the local variance was assumed to be a piecewise linear function of strike. In this article, the authors consider three piecewise linear models: the local variance as a function of strike, the local variance as a function of log-strike, and the local volatility as a function of strike (so, the local variance is a piecewise quadratic function of strike). The authors show that for all these new constructions, it is still possible to derive an ordinary differential equation for the option price, which plays the role of Dupire’s equation for the standard local volatility model, and moreover, it can be solved in closed form. Finally, similar to in Carr and Itkin, the authors show that given multiple smiles the whole local variance/volatility surface can be recovered without requiring solving any optimization problem. Instead, it can be done term-by-term by solving a system of nonlinear algebraic equations for each maturity, which is a significantly faster process. TOPICS:Derivatives, statistical methods, options Key Findings • An extension of the Local Variance Gamma model is proposed on the basis of the Geometric Brownian motion with drift. • Three piecewise linear models: the local variance as a function of strike, the local variance as function of log-strike, and the local volatility as a function of strike (so, the local variance is a piecewise quadratic function of strike) are considered. • For all these new constructions an ODE is derived which replaces the Dupire equation and can be solved in closed form.
Meta-study of laser power calibrations ranging 20 orders of magnitude with traceability to the kilogram*

Laser power metrology at the National Institute of Standards and Technology (NIST) ranges 20 orders of magnitude from photon-counting (1000 photons/s) to 100 kW (10^23 photons/s at a wavelength of 1070 nm). As a part of routine practices, we perform internal (unpublished) comparisons between our various power meters to verify correct operation.
Path Counting and Rank Gaps in Differential Posets

We study the gaps Δpn between consecutive rank sizes in r-differential posets by introducing a projection operator whose matrix entries can be expressed in terms of the number of certain paths in the Hasse diagram. We strengthen Miller’s result that Δpn ≥ 1, which resolved a longstanding conjecture of Stanley, by showing that Δpn ≥ 2r. We also obtain stronger bounds in the case that the poset has many substructures called threads.
Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation

We consider the problem of distributedly computing a general class of functions, referred to as gradient-type computation, while maintaining the privacy of the input dataset. Gradient-type computation evaluates the sum of some "partial gradients", defined as polynomials of subsets of the input. It underlies many algorithms in machine learning and data analytics. We propose Harmonic Coding, which universally computes any gradient-type function, while requiring the minimum possible number of workers. Harmonic Coding strictly improves computing schemes developed based on prior works, such as Shamir’s secret sharing and Lagrange Coded Computing, by injecting coded redundancy using harmonic progression. It enables the computing results of the workers to be interpreted as the sum of partial gradients and some redundant results, which then allows the cancellation of non-gradient terms in the decoding process. By proving a matching converse, we demonstrate the optimality of Harmonic Coding, even compared to the schemes that are non-universal (i.e., can be designed based on a specific gradient-type function).
Identifying latent mode-use propensity segments in an all-AV era

Abstract This study offers an early glimpse of how individuals perceive the advantages/disadvantages of AVs, their mode-use intentions, and potential market segments with respect to mode use, should AVs eventually become the only way to travel by car. To do so, we implemented a statewide survey of Georgia residents (N = 2890) and using that data, we applied factor analyses to two blocks of AV-related statements. The first block measured 12 perceptions of AVs, and yielded two psychological constructs: AV pros (advantages/ benefits) and AV overuse cons (negative outcomes specifically associated with the excessive use of AVs). The second block of statements measured respondents’ inclinations between AV and non-AV options for 12 hypothetical transportation “needs”, and factor analysis identified four mode-use propensity constructs: AV(-inclined) over walk/bike, AV over flight, zero-occupant AV over occupied AV, and AV over transit. The main goal of the paper was to segment the sample on the basis of these four mode-use propensities, to identify clusters with similar propensity profiles or response vectors. We applied latent class cluster analysis to do so, and identified seven potential market segments: some preferring AV options in general, others preferring non-AV options or having unique propensity patterns based on certain contexts (e.g. long distance travel and vehicle occupancy). In the model, socio-demographics, geography, attitudes, and perceptions of AVs help characterize those market segments, and this provides a basis for deeper interpretation and consideration of policy implications.
Density estimation techniques for multiscale coupling of kinetic models of the plasma material interface

Abstract In this work we analyze two classes of Density-Estimation techniques which can be used to consistently couple different kinetic models of the plasma-material interface, intended as the region of plasma immediately interacting with the first surface layers of a material wall. In particular, we handle the general problem of interfacing a continuum multi-species Vlasov-Poisson-BGK plasma model to discrete surface erosion models. The continuum model solves for the energy-angle distributions of the particles striking the surface, which are then driving the surface response. A modification to the classical Binary-Collision Approximation (BCA) method is here utilized as a prototype discrete model of the surface, to provide boundary conditions and impurity distributions representative of the material behavior during plasma irradiation. The numerical tests revealed the superior convergence properties of Gaussian Mixture Models over Kernel Density Estimation methods, with Gaussian Mixtures and Epanechnikov-KDEs both being up to two orders of magnitude faster than Gaussian-KDEs. The methodology here presented allows a self-consistent treatment of the plasma-material interface in magnetic fusion devices, including both the near-surface plasma (plasma sheath and presheath) in magnetized conditions, and surface effects such as sputtering, back-scattering, and ion implantation. The same coupling techniques can also be utilized for other discrete material models such as Molecular Dynamics.
The role of relationship scope in sustaining relational contracts in interfirm networks

Research summary A key strategic decision for many firms is the scope of their relationships with partners. Existing theories of relationship scope are limited in that they disregard the facts that: (a) most firms transact within networks of multiple partners, and (b) these partnerships often involve two‐sided moral hazard. We develop a theory of partnership scope in interfirm networks that addresses these deficiencies. We show how, by broadening the scope of business it conducts with its partners, a firm can reduce externalities between them, and thereby sustain self‐enforcing exchange relationships (“relational contracts”) in which both parties cooperate with each other repeatedly and maximize the value created. We discuss numerous settings in which our model applies, including franchising, supply chains, and platform‐based ecosystems. Managerial summary A key strategic decision for many firms is the number of transactions or activities they conduct with a given supplier, business customer, or company that sells complementary products or services. We offer a theory to explain why firms often prefer relationships with broader scope. Whereas other theories are based on the ability to leverage partner knowledge or cheaper supervision, our theory is based on the concept of a “relational contract”, in which a firm and its partner cooperate with each other repeatedly according to an informal agreement between them. We show that under relational contracting, broader scope relationships encourage better mutual cooperation than narrow scope relationships, thereby maximizing the value created by them. We discuss how our model applies to franchising, supply chains, and platform‐based ecosystems.
Experimental Observation of PT Symmetry Breaking near Divergent Exceptional Points

Standard exceptional points (EPs) are non-Hermitian degeneracies that occur in open systems. At an EP, the Taylor series expansion becomes singular and fails to converge-a feature that was exploited for several applications. Here, we theoretically introduce and experimentally demonstrate a new class of parity-time symmetric systems [implemented using radio frequency (rf) circuits] that combine EPs with another type of mathematical singularity associated with the poles of complex functions. These nearly divergent exceptional points can exhibit an unprecedentedly large eigenvalue bifurcation beyond those obtained by standard EPs. Our results pave the way for building a new generation of telemetering and sensing devices with superior performance.
Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Decision-making in dynamic environments typically requires adaptive evidence accumulation that weights new evidence more heavily than old observations. Recent experimental studies of dynamic decision tasks require subjects to make decisions for which the correct choice switches stochastically throughout a single trial. In such cases, an ideal observer's belief is described by an evolution equation that is doubly stochastic, reflecting stochasticity in the both observations and environmental changes. In these contexts, we show that the probability density of the belief can be represented using differential Chapman-Kolmogorov equations, allowing efficient computation of ensemble statistics. This allows us to reliably compare normative models to near-normative approximations using, as model performance metrics, decision response accuracy and Kullback-Leibler divergence of the belief distributions. Such belief distributions could be obtained empirically from subjects by asking them to report their decision confidence. We also study how response accuracy is affected by additional internal noise, showing optimality requires longer integration timescales as more noise is added. Lastly, we demonstrate that our method can be applied to tasks in which evidence arrives in a discrete, pulsatile fashion, rather than continuously.
Protecting and Benchmarking Migrants’ Rights: An Analysis of the Global Compact for Safe, Orderly and Regular Migration

The Global Compact for Safe, Orderly, and Regular Migration (GCM) was to be “guided by human rights law and standards” in recognition of the rights of international migrants, who are currently protected by an overlapping patchwork of treaties and international law. The GCM contains many laudable commitments that, if implemented, will ensure that states more consistently respect, protect, and fulfill the rights of all migrants and also that states incorporate data on migration into a more cohesive governance regime that does more to promote cooperation on the issue of international migration. However, many concerns remain. Using a legal analysis and cross-national policy data, we find that the GCM neither fully articulates existing law nor makes use of international consensus to expand the rights of migrants. In its first section, this paper provides a concise analysis of the GCM's compliance with a set of core principles of existing international human rights law regarding migrants. In the second section, we apply a novel instrument to create an objective, cross-national accounting of the laws protecting migrants’ rights in various national legal frameworks. Focusing on a sample of five diverse destination and sending countries, the results suggest we are close to an international consensus on the protection of a core set of migrants’ rights. This analysis should help prioritize the work necessary to implement the GCM.
High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

Background Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. Results Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this "breeder-trained" network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and very high accuracy against the "ground-truth" values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. Conclusions We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping because it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticists, pathologists, and physiologists to train the networks.
The citation disadvantage of clinical research

Abstract Biomedical research encompasses diverse types of activities, from basic science (“bench”) to clinical medicine (“bedside”) to bench-to-bedside translational research. It, however, remains unclear whether different types of research receive citations at varying rates. Here we aim to answer this question by using a newly proposed paper-level indicator that quantifies the extent to which a paper is basic science or clinical medicine. Applying this measure to 5 million biomedical papers, we find a systematic citation disadvantage of clinical oriented papers; they tend to garner far fewer citations and are less likely to be hit works than papers oriented towards basic science. At the same time, clinical research has a higher variance in its citation. We also find that the citation difference between basic and clinical research decreases, yet still persists, if longer citation-window is used. Given the increasing adoption of short-term, citation-based bibliometric indicators in funding decisions, the under-cited effect of clinical research may provide disincentives for bio-researchers to venture into the translation of basic scientific discoveries into clinical applications, thus providing explanations of reasons behind the existence of the gap between basic and clinical research that is commented as “valley of death” and the commentary of “extinction” risk of translational researchers. Our work may provide insights to policy-makers on how to evaluate different types of biomedical research.
Wavefunction Collapse and Conservation Laws

It is emphasized that the collapse postulate of standard quantum theory can violate conservation of energy-momentum and there is no indication from where the energy-momentum comes or to where it goes. Likewise, in the Continuous Spontaneous Localization (CSL) dynamical collapse model, particles gain energy on average. In CSL, the usual Schrodinger dynamics is altered so that a randomly fluctuating classical field interacts with quantized particles to cause wavefunction collapse. In this paper it is shown how to define energy for the classical field so that the average value of the energy of the field plus the quantum system is conserved for the ensemble of collapsing wavefunctions. While conservation of just the first moment of energy is, of course, much less than complete conservation of energy, this does support the idea that the field could provide the conservation law balance when events occur.
PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Reconciling Simulated Ensembles of Apomyoglobin with Experimental Hydrogen/Deuterium Exchange Data Using Bayesian Inference and Multiensemble Markov State Models.

Hydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parametrized from ultralong molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters. We next presen...
Dust Continuum Observations of Protostars: Constraining Properties with Simulations

The properties of unresolved protostars and their local environment (e.g., disk, envelope and outflow characteristics) are frequently inferred from spectral energy distributions (SEDs) through comparison with idealized model SEDs. However, if it is not possible to image a source and its environment directly, it is difficult to constrain and evaluate the accuracy of these derived properties. In this proceeding, I present a brief overview of the reliability of SED modeling by analyzing dust continuum synthetic observations of realistic simulations.
Sex Chromosome Pairing Mediated by Euchromatic Homology in Drosophila Male Meiosis.

Drosophila males have evolved a unique system of chromosome segregation in meiosis that lacks recombination. Chromosomes pair at selected sequences suggesting that early steps of meiosis may also differ in this organism... Diploid germline cells must undergo two consecutive meiotic divisions before differentiating as haploid sex cells. During meiosis I, homologs pair and remain conjoined until segregation at anaphase. Drosophila melanogaster spermatocytes are unique in that the canonical events of meiosis I including synaptonemal complex formation, double-strand DNA breaks, and chiasmata are absent. Sex chromosomes pair at intergenic spacer sequences within the ribosomal DNA (rDNA). Autosomes pair at numerous euchromatic homologies, but not at heterochromatin, suggesting that pairing may be limited to specific sequences. However, previous work generated from genetic segregation assays or observations of late prophase I/prometaphase I chromosome associations fail to differentiate pairing from maintenance of pairing (conjunction). Here, we separately examined the capability of X euchromatin to pair and conjoin using an rDNA-deficient X and a series of Dp(1;Y) chromosomes. Genetic assays showed that duplicated X euchromatin can substitute for endogenous rDNA pairing sites. Segregation was not proportional to homology length, and pairing could be mapped to nonoverlapping sequences within a single Dp(1;Y). Using fluorescence in situ hybridization to early prophase I spermatocytes, we showed that pairing occurred with high fidelity at all homologies tested. Pairing was unaffected by the presence of X rDNA, nor could it be explained by rDNA magnification. By comparing genetic and cytological data, we determined that centromere proximal pairings were best at segregation. Segregation was dependent on the conjunction protein Stromalin in Meiosis, while the autosomal-specific Teflon was dispensable. Overall, our results suggest that pairing may occur at all homologies, but there may be sequence or positional requirements for conjunction.
Sketching Volume Capacities in Deduplicated Storage

The adoption of deduplication in storage systems has introduced significant new challenges for storage management. Specifically, the physical capacities associated with volumes are no longer readily available. In this work, we introduce a new approach to analyzing capacities in deduplicated storage environments. We provide sketch-based estimations of fundamental capacity measures required for managing a storage system: How much physical space would be reclaimed if a volume or group of volumes were to be removed from a system (the reclaimable capacity) and how much of the physical space should be attributed to each of the volumes in the system (the attributed capacity). Our methods also support capacity queries for volume groups across multiple storage systems, e.g., how much capacity would a volume group consume after being migrated to another storage system? We provide analytical accuracy guarantees for our estimations as well as empirical evaluations. Our technology is integrated into a prominent all-flash storage array and exhibits high performance even for very large systems. We also demonstrate how this method opens the door for performing placement decisions at the data-center level and obtaining insights on deduplication in the field.
Appealing to Sense and Sensibility: System 1 and System 2 Interventions for Fake News on Social Media

Disinformation on social media—commonly called “fake news”—has become a major concern around the world, and many fact-checking initiatives have been launched in response. However, if the presentati...
The weak specification property for geodesic flows on CAT(–1) spaces

We prove that the geodesic flow on a compact locally CAT(-1) space has the weak specification property, and give various applications. We show that every Holder potential on the space of geodesics has a unique equilibrium state. We establish the equidistribution of weighted periodic orbits and the large deviations principle for all such measures. The thermodynamic results are proved for the class of expansive flows with weak specification.
Who bears the burden of long-lived molecular biology databases?

In the early 1990s the life sciences quickly adopted online databases to facilitate wide-spread dissemination and use of scientific data. Starting in 1991, the journal Nucleic Acids Research published an annual Database Issue dedicated to articles describing molecular biology databases. Analysis of these articles reveals a set of long-lived databases which have remained available for more than 15 years. Given the pervasive challenge of sustaining community resources, these databases provide an opportunity to examine what factors contribute to persistence by addressing two questions 1) which organizations fund these long-lived databases? and 2) which organizations maintain these long-lived databases? Funding and operating organizations for 67 databases were determined through review of Database Issue articles. The results reveal a diverse set of contributing organizations with financial and operational support spread across six categories: academic, consortium/collective, government, industry, philanthropic, and society/association. The majority of databases reported support from more than one funding organization, of which government organizations were most common source of funds. Operational responsibilities were more distributed, with academic organizations serving as the most common hosts. Although there is evidence of diversification overall, the most acknowledged funding and operating organizations contribute to disproportionately large percentages of the long-lived databases investigated here.
Switchgrass Rhizosphere Metabolite Chemistry Driven by Nitrogen Availability

Plants and soil microorganisms interact closely in the rhizosphere where plants may exchange carbon (C) for functional benefits from the microbial community. For example, the bioenergy crop, switch...
Global Synchronization of Clocks in Directed Rooted Acyclic Graphs: A Hybrid Systems Approach

In this paper, we study the problem of robust global synchronization of resetting clocks in multi-agent networked systems, where by robust global synchronization we mean synchronization that can be achieved from all initial conditions and is insensitive to small perturbations. In particular, we address the following question: Given a set of homogeneous agents with periodic clocks, what kind of information flow topologies will guarantee that the resulting networked systems can achieve robust global synchronizationƒ To address the question, we rely on the use of robust hybrid dynamical systems. Using the hybrid-system approach, we provide a partial solution to the question: Specifically, we show that one can achieve robust global synchronization if the underlying information flow topology is a rooted acyclic digraph. Such a result is complementary to the existing results in [1] and [2] by Poveda & Teel, in which strongly connected digraphs are considered as the underlying information flow topologies of the networked systems. We have further computed an upper bound on the convergence time for a networked system to reach global synchronization. In particular, the computation reveals the relationship between convergence time and the structure of the underlying digraph. We illustrate our theoretical findings via numerical simulations toward the end of the paper.
Exact Line Packings from Numerical Solutions

Recent progress in Zauner’s conjecture has leveraged deep conjectures in algebraic number theory to promote numerical line packings to exact and verifiable solutions to the line packing problem. We introduce a numerical-to-exact technique in the real setting that does not require such conjectures. Our approach is completely reproducible, matching Sloane’s database of putatively optimal numerical line packings with Mathematica’s built-in implementation of cylindrical algebraic decomposition. As a proof of concept, we promote a putatively optimal numerical packing of eight points in the real projective plane to an exact packing, whose optimality we establish in a forthcoming paper.
Diffraction tomography with a deep image prior

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.
How does individual variation in sociality influence fitness in prairie voles

Comparative studies aid in our understanding of specific conditions favouring the initial evolution of different types of social behaviours, yet there is much unexplained intraspecific variation in the expression of social behaviour that comparative studies have not yet addressed. The proximate causes of this individual variation in social behaviour within a species have been examined in some species but its fitness consequences have been less frequently investigated. In this study, we quantified the fitness consequences of variation in the sociality of prairie voles, Microtus ochrogaster. We characterized sociality of voles in seminatural enclosures using an automated behavioural tracking system paired with social network analyses to quantify the degree of spatial and temporal co-occurrence of different voles. We then assessed the relationship between sociality and both mating success (number of different conspecifics with which an individual produced offspring) and reproductive success (total number of offspring surviving to first capture). We measured the number of social connections each individual had with all voles and with only opposite-sex voles (unweighted degree) through social network analyses. Both female and male voles varied in the number of social connections they had with all conspecifics and with opposite-sex conspecifics. In both analyses, females and males with an intermediate number of social connections had higher mating success overall and, for the analysis with all connections, produced more offspring. Males with many or few social connections also had the lowest average body mass. Overall, our results suggest some limit on the fitness benefits of sociality. Although there was substantial individual variation in our measure of vole social behaviour, intermediate levels of social connections may be most favourable.
Learning in the machine: To share or not to share?

Abstract Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization approach, it is not a necessity in computer vision applications. Furthermore, Free Convolutional Networks match the performance observed in standard architectures when trained using properly translated data (akin to video). Under the assumption of translationally augmented data, Free Convolutional Networks learn translationally invariant representations that yield an approximate form of weight-sharing.
Sketching for Motzkin’s Iterative Method for Linear Systems

Projection-based iterative methods for solving large over-determined linear systems are well-known for their simplicity and computational efficiency. It is also known that the correct choice of a sketching procedure (i.e., preprocessing steps that reduce the dimension of each iteration) can improve the performance of iterative methods in multiple ways, such as, to speed up the convergence of the method by fighting inner correlations of the system, or to reduce the variance incurred by the presence of noise. In the current work, we show that sketching can also help us to get better theoretical guarantees for the projection-based methods. Specifically, we use good properties of Gaussian sketching to prove an accelerated convergence rate of the sketched relaxation (also known as Motzkin’s) method. The new estimates hold for linear systems of arbitrary structure. We also provide numerical experiments in support of our theoretical analysis of the sketched relaxation method.
Exact Byzantine Consensus on Arbitrary Directed Graphs Under Local Broadcast Model.

We consider Byzantine consensus in a synchronous system where nodes are connected by a network modeled as a directed graph, i.e., communication links between neighboring nodes are not necessarily bi-directional. The directed graph model is motivated by wireless networks wherein asymmetric communication links can occur. In the classical point-to-point communication model, a message sent on a communication link is private between the two nodes on the link. This allows a Byzantine faulty node to equivocate, i.e., send inconsistent information to its neighbors. This paper considers the local broadcast model of communication, wherein transmission by a node is received identically by all of its outgoing neighbors. This allows such neighbors to detect a faulty node's attempt to equivocate, effectively depriving the faulty nodes of the ability to send conflicting information to different neighbors. Prior work has obtained sufficient and necessary conditions on undirected graphs to be able to achieve Byzantine consensus under the local broadcast model. In this paper, we obtain tight conditions on directed graphs to be able to achieve Byzantine consensus with binary inputs under the local broadcast model. The results obtained in the paper provide insights into the trade-off between directionality of communication and the ability to achieve consensus.
Development of a genetic toolset for the highly engineerable and metabolically versatile Acinetobacter baylyi ADP1

One primary objective of synthetic biology is to improve the sustainability of chemical manufacturing. Naturally occurring biological systems can utilize a variety of carbon sources, including waste streams that pose challenges to traditional chemical processing, such as lignin biomass, providing opportunity for remediation and valorization of these materials. Success, however, depends on identifying micro-organisms that are both metabolically versatile and engineerable. Identifying organisms with this combination of traits has been a historic hindrance. Here, we leverage the facile genetics of the metabolically versatile bacterium Acinetobacter baylyi ADP1 to create easy and rapid molecular cloning workflows, including a Cas9-based single-step marker-less and scar-less genomic integration method. In addition, we create a promoter library, ribosomal binding site (RBS) variants and test an unprecedented number of rationally integrated bacterial chromosomal protein expression sites and variants. At last, we demonstrate the utility of these tools by examining ADP1's catabolic repression regulation, creating a strain with improved potential for lignin bioprocessing. Taken together, this work highlights ADP1 as an ideal host for a variety of sustainability and synthetic biology applications.
Some extension algebras for standard modules over KLR algebras of type A

Abstract Khovanov-Lauda-Rouquier algebras R θ of finite Lie type are affine quasihereditary with standard modules Δ ( π ) labeled by Kostant partitions of θ. Let Δ be the direct sum of all standard modules. It is known that the Yoneda algebra E θ : = Ext R θ ⁎ ( Δ , Δ ) carries a structure of an A ∞ -algebra which can be used to reconstruct the category of standardly filtered R θ -modules. In this paper, we explicitly describe E θ in two special cases: (1) when θ is a positive root in type A , and (2) when θ is of Lie type A 2 . In these cases, E θ turns out to be torsion free and intrinsically formal. We provide an example to show that the A ∞ -algebra E θ is non-formal in general.
Effective protection redux

Abstract This paper rehabilitates effective protection for use in political economy. In general equilibrium, the usual definition corresponds to no economically interesting magnitude. The effective rate of protection for sector j is redefined here as the uniform tariff which is equivalent to the actual differentiated tariff structure in its effect on rents to residual claimants in sector j . It permits political economic ranking of `how much protection' is given across sectors, since higher uniform tariffs imply higher losses of welfare sacrificed to the interest groups. Demonstrating operationality, the numerical results for the US economy for the old and new concepts are not significantly correlated.
A short proof of Bernoulli disjointness via the local lemma

Recently, Glasner, Tsankov, Weiss, and Zucker showed that if $\Gamma$ is an infinite discrete group, then every minimal $\Gamma$-flow is disjoint from the Bernoulli shift $2^\Gamma$. Their proof is somewhat involved; in particular, it invokes separate arguments for different classes of groups. In this note, we give a short and self-contained proof of their result using purely combinatorial methods applicable to all groups at once. Our proof relies on the Lovasz Local Lemma, an important tool in probabilistic combinatorics that has recently found several applications in the study of dynamical systems.
Cardiac Disease in Pregnancy

Pregnancy brings about profound alterations in the maternal cardiovascular system. The pregnant patient with normal cardiac function accommodates such physiologic changes without difficulty. However, in the presence of significant cardiac disease, pregnancy can be extremely hazardous and may result in decompensation and death. Indeed, in the presence of cardiac disease involving pulmonary hypertension, pregnancy carries a risk of maternal mortality (30–50%) exceeding that which is associated with any other systemic disease. This chapter focuses on the interaction between structural cardiac disease and pregnancy.
Estimating Fungibility Between Skills by Combining Skill Similarities Obtained from Multiple Data Sources

This paper proposes an approach to estimating fungibility between skills given multiple information sources of those skills. An estimate of skill adjacency or fungibility or substitutability is critical for effective capacity planning, analytics and optimization in the face of changing skill requirements of an organization. The proposed approach is based on computing a similarity measure between skills, using each available data source, and combining these similarities into a measure of fungibility. We present both supervised and unsupervised integration methods and demonstrate that these produce improved outcomes, compared to using any single skill similarity source alone, using data from a large IT organization. The skills’ fungibility matrix created using this approach has been deployed by the organization for demand forecasting across groups of skills. We discuss how the fungibility matrix is deployed to generate skill clusters and present a forecasting algorithm that additionally incorporates past/future engagements and a mechanism to quantify uncertainty in the forecast. A possible extension of this work is to use the fungibility measure to cluster skills and develop a skill-centric representation of an organization to enable strategic assessments and planning.
The Needfinding Machine.

Robotics and automation technologies have begun to extend ubiquitous computing to include interactive objects with physical and social agency. My thesis, the Needfinding Machine, explores using social robots as design tools for understanding people and creating rich adaptive user experiences. In essence, the Needfinding Machine enables a conversation between a designer and a person \textit{through} a social robot. This conversation can support designers during needfinding and enable future social robots to elicit meaningful information from users.
Reliability Analysis of Structural Ceramic Components Using a Three-Parameter Weibull Distribution

Described here are nonlinear regression estimators for the three-Weibull distribution. Issues relating to the bias and invariance associated with these estimators are examined numerically using Monte Carlo simulation methods. The estimators were used to extract parameters from sintered silicon nitride failure data. A reliability analysis was performed on a turbopump blade utilizing the three-parameter Weibull distribution and the estimates from the sintered silicon nitride data.