cssi/SciSciGPT-SciSciNet huggingface dataset:
Micro and Macro Crack Formation

The formation of cleavage microcracks with a length of the order of one grain diameter is considered to be the initial step in fracture. It is assumed that the stress concentration required for cleavage is supplied by thick slip or twin bands, and the critical width of these yield bands is calculated. For example, in iron with a grain radius of 10−2 cm, the critical slip band width is 2 × 10−5 cm, and this value is compatible with observations in the vicinity of microcracks. The second stage of crack formation involves the semi-continuous propagation of microcracks to form unstable macroscopic cracks. We postulate that plane strain fractures occur under conditions where thick slip bands are formed in the yielded region in front of an advancing crack. Work required to extend the initial crack is used to calculate the crack extension force, GIc, which is required in linear fracture mechanics. In the case of iron, the microcrack extension force, Y, is calculated to be 5 × 103 dynes/cm, and the minimum value of GIc is calculated to be 2.5 × 106 dynes/cm. This approach emphasizes the three conditions required for fracture: 1) a combination of stress and yield band width sufficient to cause local cleavage; 2) sufficient mechanical energy in the system to propagate the crack; 3) the development of a critical value of the initiation stress in order to continue crack extension. These concepts are used to estimate the plane strain transition and the nominal stress for fracture, i.e. plate fracture stress, σn, is estimated from an elastic-plastic stress analysis to be σc/4, where σc is the yield stress at the tensile transition temperature. The tensile transition is chosen as the point at which the yield and fracture stress are about equal, and the plate transition temperature corresponds to the temperature, TC, at which the yield stress has a value of σc/4. We also estimate that crack arrest in steel plates corresponds to an energy absorption, GC = 22.5 × 103 × t/d, where GC is the crack extension force at the transition between plane strain and plane stress (dynes/cm), t is the plate thickness (cm), and d is the grain radius (cm). A reasonably good correlation for GIc and GC calculated and tested, is obtained. We also use tensile transition data to estimate a plate transition temperature and a critical tensile stress for crack propagation. These are combined with a suggested minimum value of the crack extension force, GC (arrest), to provide the basis for a fracture-safe design criterion.
Supporting caregivers of veterans with Alzheimer's disease and traumatic brain injury: study protocol for a randomized controlled trial.

Patients with Alzheimer’s disease and related dementias (ADRD) and traumatic brain injury (TBI) and their caregivers require cognitive and behavioral symptom management, interdisciplinary care, support for caregivers, and seamless care coordination between providers. Caring for someone with ADRD or TBI is associated with higher rates of psychological morbidity and burden, social isolation, financial hardship, and deterioration of physical health. Tremendous need exists for primary care–based interventions that concurrently address the care needs of dyads and aim to improve care and outcomes for both individuals with ADRD and TBI and their family caregivers. The Aging Brain Care Acquiring New Skills While Enhancing Remaining Strengths (ABC ANSWERS) study is a randomized controlled trial that tests the effectiveness of an intervention based on two evidence-based programs that have been developed for and previously tested in populations with ADRD, TBI, stroke, and late-life depression and/or who have survived an intensive care unit stay. This study includes 200 dyads comprised of a veteran with a diagnosis of ADRD or TBI and the veteran’s primary informal caregiver. Dyads are randomized to receive the ABC ANSWERS intervention or routine Veterans Health Administration (VHA) primary care with a standardized educational and resource information packet. Data collection occurs at baseline and three follow-up time points (3 months, 6 months, and 12 months). The primary outcome is caregiver quality of life (QoL). A secondary measure for the caregiver is caregiver burden. Secondary measures for both the veteran and caregiver include symptoms of depression and anxiety. The ABC ANSWERS intervention integrates common features of an evidence-based collaborative care model for brain health while concurrently attending to the implementation barriers of delivering care and skills to dyads. We hypothesize that caregivers in dyads randomized to the ABC ANSWERS program will experience higher levels of QoL and lower levels of depression, anxiety, dyadic strain, and caregiver burden at 12 months than those receiving usual VHA primary care. ClinicalTrials.gov, NCT03397667. Registered on 12 January 2018.
Critical Assessment of Single-Use Ureteroscopes in an In Vivo Porcine Model.

Introduction and Objectives. In recent years, there has been increasing utilization of single-use ureteroscopes albeit with few studies comparing the performance characteristics between these devices. We compared two commonly utilized single-use ureteroscopes in a porcine model. Methods. A female pig was placed under general anesthesia and positioned supine, and retrograde access to the renal collecting system was obtained. The LithoVue (Boston Scientific) and Uscope (Pusen Medical) were evaluated by three experienced surgeons, and each surgeon started with a new scope. The following parameters were compared between each ureteroscope: time for navigation to upper and lower pole calyces with and without implements (1.9 F basket, 200 μm laser fiber, and 365 μm laser fiber for upper only) in the working channel and subjective evaluations of maneuverability, irrigant flow through the scope, lever force, ergonomics, and scope optics. Results. Navigation to the lower pole calyx was significantly faster with LithoVue compared to Uscope when the working channel was empty (24.3 vs. 49.4 seconds, ) and with a 200 μm fiber (63.6 vs. 94.4 seconds, ), but not with the 1.9 F basket. Navigation to the upper pole calyx was similar for all categories except faster with LithoVue containing the 365 μm fiber (67.1 vs. 99.7 seconds, ). Subjective assessments of scope maneuverability to upper and lower pole calyces when the scope was empty and with implements favored LithoVue in all categories, as did assessments of irrigant flow, illumination, image quality, and field of view. Both scopes had similar scores of lever force and ergonomics. Conclusions. In an in vivo porcine model, the type of single-use ureteroscope employed affected the navigation times and subjective assessments of maneuverability and visualization. In all cases, LithoVue provided either equivalent or superior metrics than Uscope. Further clinical studies are necessary to determine the implications of these findings.
Recognition without ethics

FOR SOME time now, the forces of progressive politics have been divided into two camps. On one side stand the proponents of ‘redistribution’. Drawing on long traditions of egalitarian, labor and socialist organizing, political actors aligned with this orientation seek a more just allocation of resources and goods. On the other side stand the proponents of ‘recognition’. Drawing on newer visions of a ‘difference-friendly’ society, they seek a world where assimilation to majority or dominant cultural norms is no longer the price of equal respect. Members of the first camp hope to redistribute wealth from the rich to the poor, from the North to the South, and from the owners to the workers. Members of the second, in contrast, seek recognition of the distinctive perspectives of ethnic, ‘racial’, and sexual minorities, as well as of gender difference. The redistribution orientation has a distinguished philosophical pedigree, as egalitarian redistributive claims have supplied the paradigm case for most theorizing about social justice for the past 150 years. The recognition orientation has recently attracted the interest of political philosophers, however, some of whom are seeking to develop a new normative paradigm that puts recognition at its center. At present, unfortunately, relations between the two camps are quite strained. In many cases, struggles for recognition are dissociated from struggles for redistribution. Within social movements such as feminism, for example, activist tendencies that look to redistribution as the remedy for male domination are increasingly dissociated from tendencies that look instead to recognition of gender difference. And the same is largely true in the intellectual sphere. In the academy, to continue with feminism, scholars who understand gender as a social relation maintain an uneasy arm’s-length coexistence with those who construe it as an identity or a cultural code. This situation exemplifies a broader phenomenon: the widespread decoupling of cultural politics from social politics, of the politics of difference from the politics of equality.
Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN–S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN–S model with the implementation of various machine learning algorithms is proposed. This refined BN–S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN–S model. The procedure and the model show the application of data science for extracting a “deterministic component” out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.
Three-Dimensional Cooling of an Atom-Beam Source for High-Contrast Atom Interferometry

We present a compact, two-stage atomic beam source that produces a continuous, narrow, collimated, and high-flux beam of rubidium atoms with sub-Doppler temperatures in three dimensions, which features very low emission of near-resonance fluorescence along the atomic trajectory. The atom-beam source originates in a pushed two-dimensional magneto-optical trap ($2{\mathrm{D}}^{+}$ MOT) feeding a slightly off-axis three-dimensional moving optical-molasses stage that continuously cools and redirects the atom beam. The capture velocity of the moving optical molasses is deliberately chosen to be low, approximately $3\phantom{\rule{0.2em}{0ex}}\mathrm{m}/\mathrm{s}$, to reduce fluorescence, and the cooling light is detuned by several atomic linewidths from resonance to reduce the absorption cross section of cooling-induced fluorescence. Near-resonance light from the $2{\mathrm{D}}^{+}$ MOT and the push beam does not propagate to the output atomic trajectory due to a ${10}^{\ensuremath{\circ}}$ bend in the atomic trajectory. The atomic beam emitted from the two-stage source has a flux up to $1.6(3)\ifmmode\times\else\texttimes\fi{}{10}^{9}\phantom{\rule{0.2em}{0ex}}\mathrm{atoms}/\mathrm{s}$, with an optimized temperature of $15.0(2)\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{K}$. We employ continuous Raman-Ramsey interference measurements at the atom-beam output to study the sources of decoherence in the presence of continuous cooling, and demonstrate that the atom-beam source effectively preserves high fringe contrast even during cooling. This cold-atom-beam source is appropriate for use in atom interferometers and clocks, where continuous operation eliminates dead time, the slow atom-beam velocity (6--16 m/s) improves sensitivity, the narrow 3D velocity distribution improves fringe contrast, and the low reabsorption of scattered light mitigates decoherence caused by the continuous cooling process.
Finding convex edge groupings in an image

In an image, there are groups of intensity edges that are likely to have resulted from the same convex object in a scene. A new method for identifying such groups is described here. Groups of edges that form a convex polygonal chain, such as a convex polygon or a spiral, are extracted from a set of image edge fragments. A key property of the method is that its output is no more complex than the original image. The method uses a triangulation of the linear edge segments in an image to define a local neighborhood that is scale invariant. From this local neighborhood a local convexity graph is constructed; this encodes which neighboring image edges could be part of a convex group. A path in the graph corresponds to a convex polygonal chain in the image, with a cyclic path corresponding to a polygon. We have implemented the method and found that it is efficient in practice as well as in theory. Examples are presented to illustrate that the technique finds intuitively salient groups, including for images of cluttered scenes.
Reflection and Recovery from Psychosis during the Time of COVID-19: Adaptation in Psychotherapy in the United States

espanolRESUMEN En respuesta a la pandemia del coronavirus (COVID-19) varias adaptaciones han permitido que sigamos facilitando una de las formas de psicoterapia orientada a la recuperacion de las personas que padecen psicosis: la terapia de percepcion metacognitiva y reflexion (MERIT). Estas adaptaciones satisfactorias incluyen la incorporacion de la experiencia de los pacientes de la pandemia y el uso de los desafios que plantean los cambios temporales en las plataformas terapeuticas con objeto de profundizar en la reflexion sobre la autoexperiencia de los pacientes, su experiencia en intersubjetividad y sus propias respuestas a los desafios psicosociales. EnglishABSTRACT In response to the coronavirus (COVID-19) pandemic several adaptations have allowed us to continue to provide one form of recovery-oriented psychotherapy to persons with psychosis: Metacognitive Insight and Reflection Therapy (MERIT). These successful adaptations have included the incorporation of patients' experience of the pandemic and the exploration of challenges from temporary changes in therapy platforms to deepen reflections about patients' self-experience, their experience of intersubjectivity and their own agentic responses to psychosocial challenges.
Time-resolved ultrafast transient polarization spectroscopy to investigate nonlinear processes and dynamics in electronically excited molecules on the femtosecond time scale

We report a novel experimental technique to investigate ultrafast dynamics in photoexcited molecules by probing the 3rd-order nonlinear optical susceptibility. A non-collinear 3-pulse scheme is developed to probe the ultrafast dynamics of excited electronic states using the optical Kerr effect. Optical homodyne and optical heterodyne detections are demonstrated to measure the 3rd-order nonlinear optical response for the S1 excited state of liquid nitrobenzene, which is populated by 2-photon absorption of a 780 nm 40 fs excitation pulse.
A Configuration Management Model for Software Product Line

Software Product Line has proved to be an effective approach to benefit from software reuse. Configuration management, an integral part of any software development activity, takes on a special significance in software product line context. This is due to the special property of software product line, in which the core assets are shared by all products. In this paper, we compare the existing configuration management models and analyze the artifacts that need to be configuration managed in software product line. We then present an evolution-based configuration management model for software product line, in which, the configuration management is divided into two domains, the production domain and the product domain. In this model, the evolution propagation of corrective changes and enhancement changes on different configuration artifacts follow different paths. The advantages and the constraints of this model are also discussed.
Housing markets and residential segregation: Impacts of the Michigan school finance reform on inter- and intra-district sorting

Abstract Local financing of public schools in the U.S. leads to a bundling of two distinct choices – residential choice and school choice – and has been argued to increase the degree of socioeconomic segregation across school districts. A school finance reform, aimed at equalization of school finances, can in principle weaken this link between housing choice and choice of schools. In this paper, we study the impacts of the Michigan school finance reform of 1994 (Proposal A) on spatial segregation. The reform was a state initiative intended to equalize per pupil expenditures between Michigan school districts and reduce the role of local financing. We find that Proposal A led to a decline in neighborhood sorting within education markets, as measured by changes in the value of housing stock and several socioeconomic indicators. We also find that the reform affected the dispersion of incomes and educational attainment within school districts, increasing within-district heterogeneity in the lowest spending school districts, while decreasing the same in the highest spending districts. However, there is a continued high demand for residence in the highest spending communities. These findings are robust to various alternative definitions of “education market”, and survive several sensitivity checks. These spatial segregation patterns are not replicated in neighboring Ohio that did not face similar school finance equalization.
Microring resonator-coupled photoluminescence from silicon W centers

Silicon defect centers are promising candidates for waveguide-integrated silicon light sources. We demonstrate microresonator- and waveguide-coupled photoluminescence from silicon W centers. Microphotoluminescence measurements indicate wavelengths on-resonance with resonator modes are preferentially coupled to an adjacent waveguide. Quality factors of at least 5,300 are measured, and free spectral ranges closely match expectation. The W center phonon sideband can be used as a spectral diagnostic for a broader range of waveguide-based devices on cryogenic silicon photonic platforms.
Unsupervised Methods for Identifying Pass Coverage Among Defensive Backs with NFL Player Tracking Data

Statistical analysis of defensive players in football has lagged behind that of offensive players, special teams, and coaching decisions, largely because data on individual defensive players has historically been lacking. With the introduction of player tracking data from the NFL, researchers can now tackle these problems. However, event and strategy annotations in the NFL’s tracking data are limited, especially when it comes to describing what defensive players do on each play. Moreover, methods for creating these annotations typically require extensive human labeling, which is difficult and expensive. Because of the importance of the passing game and the limited prior research on the defensive side of passing, we provide annotations for the pass coverage types of cornerbacks using unsupervised learning techniques, which require no training data. We define a set of features from the tracking data that distinguish between “man” and “zone” coverage. We use mixture models to create clusters corresponding to each group, allowing us to provide probabilistic assignments to each coverage type (or cluster). Additionally, we quantify each feature’s influence in distinguishing defensive pass coverage types. Our work makes possible several potential avenues of future NFL research into defensive backs and pass coverage strategies.
Cost-effectiveness of various maintenance therapies based on mutation status following first-line treatment of primary epithelial ovarian cancer in the United States.

e19399Background: The recent SOLO1, PRIMA, and PAOLA trials all reported positive efficacy results, making it difficult to determine optimal upfront maintenance therapy for patients with primary, a...
Mucosal Microbiota and Metabolome along the Intestinal Tract Reveal a Location-Specific Relationship

The intestinal microbiota is highly metabolically active and plays an important role in many metabolic processes absent from the human host. Altered microbiota metabolism has been linked to diseases such as obesity, cardiovascular disease, and colorectal cancer. However, there is a gap in the current knowledge on how the microbiota interact with its host in terms of metabolic interactions. Here, we performed an integrated analysis between the mucosa-associated microbiota and the mucosa metabolome in healthy, nonhuman primates to investigate these relationships. The microbiota composition was distinct at each tissue location, with variation by host individual also observed. Microbiota-metabolome dynamics were primarily driven by interactions in the distal colon. These interactions were strongly correlated with dietary component, indicating a possibility to modulate microbiota-metabolomic interactions using prebiotic strategies.IMPORTANCE In a healthy colon, the microbiota produces a vast amount of metabolites that are essential to maintaining homeostasis in the colon microenvironment. In fact, these metabolites produced by the microbiota have been linked to diseases such as obesity, cardiovascular disease, and colorectal cancer. In this study, we used healthy nonhuman primate models to investigate the relationship between microbiota and tissue metabolites. We found that both microbiota and metabolites have location-specific signatures along the intestine. Most importantly, we found that metabolites from food sources correlate with multiple bacteria in different intestinal locations. Overall, this work presents a systems-level map of the association between the microbiota and the metabolites in healthy nonhuman primates, provides candidates for experimental validation, and suggests a possibility to regulate the gut microbiota through specific prebiotic combinations.
Group roles in unstructured labs show inequitable gender divide

In introductory labs without sufficient structure men and women systematically take on different group roles and men behave differently in groups with other men versus when there is at least one woman.
A Transformer-based Approach for Source Code Summarization

Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens' position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available (https://github.com/wasiahmad/NeuralCodeSum) to facilitate future research.
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration

This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration. In contrast to existing approaches that learn spatial transformations from training data in the high dimensional imaging space, we develop a new registration network entirely in a low dimensional bandlimited space. This dramatically reduces the computational cost and memory footprint of an expensive training and inference. To achieve this goal, we first introduce complex-valued operations and representations of neural architectures that provide key components for learning-based registration models. We then construct an explicit loss function of transformation fields fully characterized in a bandlimited space with much fewer parameterizations. Experimental results show that our method is significantly faster than the state-of-the-art deep learning based image registration methods, while producing equally accurate alignment. We demonstrate our algorithm in two different applications of image registration: 2D synthetic data and 3D real brain magnetic resonance (MR) images.
AntiDote: attention-based dynamic optimization for neural network runtime efficiency

Convolutional Neural Networks (CNNs) achieved great cognitive performance at the expense of considerable computation load. To relieve the computation load, many optimization works are developed to reduce the model redundancy by identifying and removing insignificant model components, such as weight sparsity and filter pruning. However, these works only evaluate model components' static significance with internal parameter information, ignoring their dynamic interaction with external inputs. With per-input feature activation, the model component significance can dynamically change, and thus the static methods can only achieve sub-optimal results. Therefore, we propose a dynamic CNN optimization framework in this work. Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout. Such a dynamic optimization framework has several benefits: (1) First, it can accurately identify and aggressively remove per-input feature redundancy with considering the model-input interaction; (2) Meanwhile, it can maximally remove the feature map redundancy in various dimensions thanks to the multi-dimension flexibility; (3) The training-testing co-optimization favors the dynamic pruning and helps maintain the model accuracy even with very high feature pruning ratio. Extensive experiments show that our method could bring 37.4%~54.5% FLOPs reduction with negligible accuracy drop on various of test networks.
Infinite prime avoidance

We investigate prime avoidance for an arbitrary set of prime ideals in a commutative ring. Various necessary and/or sufficient conditions for prime avoidance are given, which yield natural classes of infinite sets of primes that satisfy prime avoidance. Examples and counterexamples are given throughout to illustrate the phenomena that can occur. As an application, we show how to use prime avoidance to construct counterexamples among rings essentially of finite type.
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning

When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents. Recently proposed deep multi-agent reinforcement learning methods have tried to mitigate this non-stationarity by attempting to determine which samples are from other agent exploration or suboptimality and take them less into account during learning. Based on the same philosophy, this paper introduces a decentralized quantile estimator, which aims to improve performance by distinguishing non-stationary samples based on the likelihood of returns. In particular, each agent considers the likelihood that other agent explorations and policy changes are occurring, essentially utilizing the agent's own estimations to weigh the learning rate that should be applied towards the given samples. We introduce a formal method of calculating differences of our return distribution representations and methods for utilizing it to guide updates. We also explore the effect of risk-seeking strategies for adjusting learning over time and propose adaptive risk distortion functions that guide risk sensitivity. Our experiments, on traditional benchmarks and new domains, show our methods are more stable, sample efficient and more likely to converge to a joint optimal policy than previous methods.
Communication-Optimal Tilings for Projective Nested Loops with Arbitrary Bounds

Abstract Reducing communication - either between levels of a memory hierarchy or between processors over a network - is a key component of performance optimization (in both time and energy) for many nested loop problems, including dense linear algebra, particle interactions, and machine learning. Previous tiling based approaches for these problems have been used to find both lower bounds on the communication required to execute them and optimal rearrangements, or blockings, to attain such lower bounds. However, such general approaches have typically assumed the problem sizes are large, an assumption that is often not met in practice. In this paper, we provide an efficient way to both find and obtain, via an appropriate, efficiently constructible blocking, communication lower bounds and matching tilings which attain these lower bounds for nested loop programs with arbitrary loop bounds that operate on multidimensional arrays in the projective case, where the array indices are subsets of the loop indices. Our approach works on all such problems, regardless of dimensionality, size, memory access patterns, or number of arrays.
A stochastic Prékopa–Leindler inequality for log-concave functions

The Brunn–Minkowski and Prekopa–Leindler inequalities admit a variety of proofs that are inspired by convexity. Nevertheless, the former holds for compact sets and the latter for integrable functio...
Linear operators, the Hurwitz zeta function and Dirichlet L-functions

Abstract At the 1900 International Congress of Mathematicians, Hilbert claimed that the Riemann zeta function is not the solution of any algebraic ordinary differential equation its region of analyticity [5] . In 2015, Van Gorder addresses the question of whether the Riemann zeta function satisfies a non-algebraic differential equation and constructs a differential equation of infinite order which zeta satisfies [7] . However, as he notes in the paper, this representation is formal and Van Gorder does not attempt to claim a region or type of convergence. In this paper, we show that Van Gorder's operator applied to the zeta function does not converge pointwise at any point in the complex plane. We also investigate the accuracy of truncations of Van Gorder's operator applied to the zeta function and show that a similar operator applied to zeta and other L-functions does converge.
Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post-flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of DATA COMETS, an open-source and web-based interactive visualization tool for post-flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in-the-wild usage. A free copy of this paper and source code are available at this http URL
Differential chromatin accessibility landscape reveals structural and functional features of the allopolyploid wheat chromosomes.

Our understanding of how the complexity of the wheat genome influences the distribution of chromatin states along the homoeologous chromosomes is limited. Using a differential nuclease sensitivity assay, we investigate the chromatin states of the coding and repetitive regions of the allopolyploid wheat genome. Although open chromatin is found to be significantly enriched around genes, the majority of MNase-sensitive regions are located within transposable elements (TEs). Chromatin of the smaller D genome is more accessible than that of the larger A and B genomes. Chromatin states of different TEs vary among families and are influenced by the TEs’ chromosomal position and proximity to genes. While the chromatin accessibility of genes is influenced by proximity to TEs, and not by their position on the chromosomes, we observe a negative chromatin accessibility gradient along the telomere-centromere axis in the intergenic regions, positively correlated with the distance between genes. Both gene expression levels and homoeologous gene expression bias are correlated with chromatin accessibility in promoter regions. The differential nuclease sensitivity assay accurately predicts previously detected centromere locations. SNPs located within more accessible chromatin explain a higher proportion of genetic variance for a number of agronomic traits than SNPs located within more closed chromatin. Chromatin states in the wheat genome are shaped by the interplay of repetitive and gene-encoding regions that are predictive of the functional and structural organization of chromosomes, providing a powerful framework for detecting genomic features involved in gene regulation and prioritizing genomic variation to explain phenotypes.
Fluctuations of the spectrum in rotationally invariant random matrix ensembles

We investigate traces of powers of random matrices whose distributions are invariant under rotations (with respect to the Hilbert–Schmidt inner product) within a real-linear subspace of the space o...
Microbial diversity drives carbon use efficiency in a model soil

Empirical evidence for the response of soil carbon cycling to the combined effects of warming, drought and diversity loss is scarce. Microbial carbon use efficiency (CUE) plays a central role in regulating the flow of carbon through soil, yet how biotic and abiotic factors interact to drive it remains unclear. Here, we combine distinct community inocula (a biotic factor) with different temperature and moisture conditions (abiotic factors) to manipulate microbial diversity and community structure within a model soil. While community composition and diversity are the strongest predictors of CUE, abiotic factors modulated the relationship between diversity and CUE, with CUE being positively correlated with bacterial diversity only under high moisture. Altogether these results indicate that the diversity × ecosystem-function relationship can be impaired under non-favorable conditions in soils, and that to understand changes in soil C cycling we need to account for the multiple facets of global changes.
Data-Driven Operator Theoretic Methods for Global Phase Space Learning

In this work, we developed new Koopman operator techniques to explore the global phase space of a nonlinear system from time-series data. In particular, we address the problem of identifying various invariant subsets of a phase space from the spectral properties of the associated Koopman operator constructed from time-series data. Moreover, in the case when the system evolution is known locally in various invariant subspaces, then a phase space stitching result is proposed that can be applied to identify a global Koopman operator. A biological system, the bistable toggle switch is considered to illustrate the proposed results. The construction of this global Koopman operator is very helpful in experimental works as multiple experiments cannot be performed at the same time starting from several initial conditions.
S-ADDOPT : Decentralized Stochastic First-Order Optimization Over Directed Graphs

In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes. In contrast to the existing work, we use gradient tracking to improve certain aspects of the resulting algorithm. In particular, we propose the S-ADDOPT algorithm that assumes a stochastic first-order oracle at each node and show that for a constant step-size $\alpha $ , each node converges linearly inside an error ball around the optimal solution, the size of which is controlled by $\alpha $ . For decaying step-sizes $\mathcal {O}$ (1/ ${k}$ ), we show that S-ADDOPT reaches the exact solution sublinearly at $\mathcal {O}$ (1/ ${k}$ ) and its convergence is asymptotically network-independent. Thus the asymptotic behavior of S-ADDOPT is comparable to the centralized stochastic gradient descent. Numerical experiments over both strongly convex and non-convex problems illustrate the convergence behavior and the performance comparison of the proposed algorithm.
Multi-modal recognition of worker activity for human-centered intelligent manufacturing

Abstract This study aims at sensing and understanding the worker’s activity in a human-centered intelligent manufacturing system. We propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armband and a visual camera are applied to capture Inertial Measurement Unit (IMU) signals and videos, respectively. For the IMU signals, we design two novel feature transform mechanisms, in both frequency and spatial domains, to assemble the captured IMU signals as images, which allow using convolutional neural networks to learn the most discriminative features. Along with the above two modalities, we propose two other modalities for the video data, i.e., at the video frame and video clip levels. Each of the four modalities returns a probability distribution on activity prediction. Then, these probability distributions are fused to output the worker activity classification result. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab a tool/part, hammer a nail, use a power-screwdriver, rest arms, turn a screwdriver, and use a wrench. The developed multi-modal approach is evaluated on this dataset and achieves recognition accuracies as high as 97% and 100% in the leave-one-out and half-half experiments, respectively.
A Cost-efficient IoT Forensics Framework with Blockchain

IoT devices have been adopted widely in the last decade which enabled collection of various data from different environments. Data storage poses challenges since the data may be compromised during the storage and the integrity might be violated without being noticed. In such cases, integrity and data provenance are required in order to be able to detect the source of any incident and prove it in legal cases. To address these issues, blockchain provides excellent opportunities since it can protect the integrity of the data thanks to its distributed structure. However, it comes with certain costs as storing huge amount of data in a public blockchain will come with significant transaction fees. In this paper, we propose a highly cost effective and reliable digital forensics framework by exploiting multiple inexpensive blockchain networks as a temporary storage before the data is committed to Ethereum. To reduce Ethereum costs, we utilize Merkle trees which hierarchically stores hashes of the collected event data from IoT devices. We evaluated the approach on popular blockchains such as EOS, Stellar, and Ethereum by presenting a cost and security analysis. The results indicate that we can achieve significant cost savings without compromising the integrity of the data.
TRTools: a toolkit for genome-wide analysis of tandem repeats.

Summary A rich set of tools have recently been developed for performing genome-wide genotyping of tandem repeats (TRs). However, standardized tools for downstream analysis of these results are lacking. To facilitate TR analysis applications, we present TRTools, a Python library and suite of command line tools for filtering, merging, and quality control of TR genotype files. TRTools utilizes an internal harmonization module making it compatible with outputs from a wide range of TR genotypers. Availability TRTools is freely available at https://github.com/gymreklab/TRTools. Documentation Detailed documentation is available at https://trtools.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.
Free energy of the quantum Sherrington-Kirkpatrick spin-glass model with transverse field

We consider the quantum Sherrington–Kirkpatrick (SK) spin-glass model with a transverse field and provide a formula for its free energy in the thermodynamic limit, valid for all inverse temperatures β > 0. To characterize the free energy, we use the path integral representation of the partition function and approximate the model by a sequence of finite-dimensional vector-spin glasses with Rd-valued spins. This enables us to use the results of Panchenko who generalized [Ann. Probab. 46(2), 829–864 (2018); ibid, 46(2), 865–896 (2018)] the Parisi formula to classical vector-spin glasses. As a consequence, we can express the thermodynamic limit of the free energy of the quantum SK model as the d → ∞ limit of the free energies of the d-dimensional approximations of the model.
Weight-based quality specialization

Abstract This paper uncovers a new type of quality specialization that occurs along the physical weight margin. To this end, I document that (i) there is great heterogeneity in the unit weight of traded goods even within narrowly-defined product categories; (ii) heavier varieties of the same product are more costly to produce; (iii) heavier varieties exhibit (on average) a higher product appeal or quality; and (iv) the cost of transportation increases more rapidly with unit weight than the cost of production. These observations indicate that suppliers face a basic quality/cost trade-off when choosing their output unit weight. As a result of this trade off, high-wage economies specialize in heavier varieties of a given good, while geographically distant economies specialize in lighter varieties (i.e., weight-based quality specialization). Micro-level trade data support these predictions and suggest that weight-based quality specialization can explain a significant portion of the cross-national variation in export prices and export quality. Moreover, accounting for the heterogeneity in export unit weights yields support for the iceberg trade cost assumption, which has proven to be elusive in the past.
Convolutional Sparse Coded Dynamic Brain Functional Connectivity

Functional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.
Female zebra finches prefer the songs of males who quickly solve a novel foraging task to the songs of males unable to solve the task.

Correlative evidence suggests that high problem-solving and foraging abilities in a mate are associated with direct fitness advantages, so it would benefit females to prefer problem-solving males. Recent work has also shown that females of several bird species who directly observe males prefer those that can solve a novel foraging task over those that cannot. In addition to or instead of direct observation of cognitive skills, many species utilize assessment signals when choosing a mate. Here, we test whether females can select a problem-solving male over a non-solving male when presented only with a signal known to be used in mate assessment: song. Using an operant conditioning assay, we compared female zebra finch (Taeniopygia guttata) preference for the songs of males that could quickly solve a novel foraging task to the songs of males that could not solve the task. Females were never housed with the test subject males whose song they heard, and the only information provided about the males was their song. We found that females elicited more songs of problem-solving males than of non-solvers, indicating that song may contain information about a male's ability to solve a novel foraging task and that naive females prefer the songs of problem-solving males.
The Risk and Return Effect of a New S&P Sector

Changes to the S&P 500 Index have been found to provide a wealth effect to the firms included or removed from the Index. On November 10, 2014, the S&P Dow Indices announced the addition of the firs...
Concordances to prime hyperbolic virtual knots

Let $$\Sigma _0,\Sigma _1$$ be closed oriented surfaces. Two oriented knots $$K_0 \subset \Sigma _0 \times [0,1]$$ and $$K_1 \subset \Sigma _1 \times [0,1]$$ are said to be (virtually) concordant if there is a compact oriented 3-manifold W and a smoothly and properly embedded annulus A in $$W \times [0,1]$$ such that $$\partial W=\Sigma _1 \sqcup -\Sigma _0$$ and $$\partial A=K_1 \sqcup -K_0$$ . This notion of concordance, due to Turaev, is equivalent to concordance of virtual knots, due to Kauffman. A prime virtual knot, in the sense of Matveev, is one for which no thickened surface representative $$K \subset \Sigma \times [0,1]$$ admits a nontrivial decomposition along a separating vertical annulus that intersects K in two points. Here we prove that every knot $$K \subset \Sigma \times [0,1]$$ is concordant to a prime satellite knot and a prime hyperbolic knot. For homologically trivial knots in $$\Sigma \times [0,1]$$ , we prove this can be done so that the Alexander polynomial is preserved. This generalizes the corresponding results for classical knot concordance, due to Bleiler, Kirby–Lickorish, Livingston, Myers, Nakanishi, and Soma. The new challenge for virtual knots lies in proving primeness. Contrary to the classical case, not every hyperbolic knot in $$\Sigma \times [0,1]$$ is prime and not every composite knot is a satellite. Our results are obtained using a generalization of tangles in 3-balls we call complementary tangles. Properties of complementary tangles are studied in detail.
From One Environment to Many: The Problem of Replicability of Statistical Inferences

Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population, equipment, pers...
Some algebras that are not silting connected

Abstract We give examples of finite-dimensional algebras A for which the silting objects in K b ( proj - A ) are not connected by any sequence of (possibly reducible) silting mutations. The argument is based on the fact that silting mutation preserves invariance under twisting by a fixed algebra automorphism, combined with the existence of spherical modules that are not invariant under such a twist.