cssi/SciSciGPT-SciSciNet huggingface dataset:
Chapter 1 – Introduction to Statistics

Publisher Summary This chapter provides an introduction and a short history of statistics. It discusses some of the people whose early work provided a foundation for the work done today. The chapter demonstrates how to apply probability theory to gain insight into real, everyday statistical problems and situations. Statistics is the art of learning from data. It is concerned with the collection of data, its subsequent description, and its analysis, which leads to conclusions. A statistical analysis begins with a given set of data such as earthquake occurrences, the unemployment rate, the gross domestic product, and the rate of inflation. Statistics can be used to describe, summarize, and analyze the given data. In cases where data is not available, statistical theory can be used to design an appropriate experiment to generate data. Statistics is categorized in descriptive and inferential statistics based on their concerned role. Sometimes the nature of the data suggests the form of the probability model that is assumed. The basis of statistical inference is the formulation of a probability model to describe the data; an understanding of statistical inference requires some knowledge of the theory of probability. Two of the most important areas of applied statistics are population biology and agriculture.
Brachial Plexus Injuries

Alain Gilbert, editor. London: Martin Dunitz; 2001. 336 pages. $125.00. The back cover of this book contains the following statement: "This is a comprehensive guide to the management of brachial plexus injuries. International experts have been assembled to comment on their areas of research and clinical experience, and the resulting volume is definitive." This volume has thirty-nine contributors, and the editor is a well-known contributor to the …
Impacts of Machining and Heat Treating Practices on Residual Stresses in Alpha-Beta Titanium Alloys

Machining and thermal processing can introduce undesirable residual stresses and distortion in titanium alloy components, and although the distribution and magnitude of these residual stresses is highly relevant for component and process design in the aerospace industry, the relationships between processing variables, processing steps, residual stress signature, and subsurface microstructures are not well understood. The current study reports on the preliminary results of experiments designed to mimic typical machining and thermal processing practices for aerospace alpha-beta Ti alloys. Traditional climb cutting and high-speed peel cutting operations are included in CNC machining experiments, and both solution treating and aging heat treatments are considered for thermal processing experiments. Characterization of samples includes strain measurement using the sin2ψ and cosine α methods with x-ray diffraction as well as microstructural characterization using traditional metallographic techniques. The results of this study show a large tool-path dependence for residual stresses in machined surfaces as well as a significant difference in the residual stress behavior for solution treated and quenched compared to solution treated and aged samples of Ti 6Al-4V alloy.
An Annotated Dataset of Coreference in English Literature

We present in this work a new dataset of coreference annotations for works of literature in English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. This dataset differs from previous coreference corpora in containing documents whose average length (2,105.3 words) is four times longer than other benchmark datasets (463.7 for OntoNotes), and contains examples of difficult coreference problems common in literature. This dataset allows for an evaluation of cross-domain performance for the task of coreference resolution, and analysis into the characteristics of long-distance within-document coreference.
Doubly robust off-policy evaluation with shrinkage

We design a new family of estimators for off-policy evaluation in contextual bandits. Our estimators are based on the asymptotically optimal approach of doubly robust estimation, but they shrink importance weights to obtain a better bias-variance tradeoff in finite samples. Our approach adapts importance weights to the quality of a reward predictor, interpolating between doubly robust estimation and direct modeling. When the reward predictor is poor, we recover previously studied weight clipping, but when the reward predictor is good, we obtain a new form of shrinkage. To navigate between these regimes and tune the shrinkage coefficient, we design a model selection procedure, which we prove is never worse than the doubly robust estimator. Extensive experiments on bandit benchmark problems show that our estimators are highly adaptive and typically outperform state-of-the-art methods.
Polylidar - Polygons From Triangular Meshes

This letter presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets, including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization. The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular mesh regions representing the shape of the point set. Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented to comparatively evaluate Polylidar speed and accuracy. Results show comparable accuracy and more than four times speedup compared to other concave polygon extraction methods.
Nonconvex Bargaining Problems: Some Recent Developments

This paper presents a survey of the recent theoretical results on solutions to nonconvex bargaining problems. We discuss rationalizability of solutions to nonconvex bargaining problems, axiomatic characterizations of the symmetric and asymmetric Nash solution, the Kalai-Smorodinsky solution, and the egalitarian solution to nonconvex bargaining problems, and applications in the context of resource allocation and of bargaining over opportunity sets.
Separation: A Cure for Abuse of Platform Dominance?

Abstract Political candidates, legislators, and academics have made proposals to separate services provided by dominant digital platforms from activities that rely on these services. Although the platforms have different economic and technical characteristics, common themes that motivate these proposals are the incentives of platforms to favor their own products and to suppress investment by imitating rivals. As has been shown in other contexts, this paper demonstrates that structural separation does not eliminate incentives for platforms to discriminate in the provision of service quality. Furthermore, the ability of vertically integrated platforms to imitate rivals does not necessarily harm consumers. Structural or functional separation can address some complaints lodged against activities by dominant platforms, but experience demonstrates that separation requirements are difficult to administer and can harm innovation. Public policy should rely on a mix of antitrust enforcement and regulation to address concerns about privacy, data security, and potential influence of major platforms in politics and the media, as well as the abuse of market power.
What's The Latest? A Question-driven News Chatbot

This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news. Key components of the system include the automatic organization of news articles into topical chatrooms, integration of automatically generated questions into the conversation, and a novel method for choosing which questions to present which avoids repetitive suggestions. We describe the algorithmic framework and present the results of a usability study that shows that news readers using the system successfully engage in multi-turn conversations about specific news stories.
Making ends meet: New functions of mRNA secondary structure

The 5' cap and 3' poly(A) tail of mRNA are known to synergistically regulate mRNA translation and stability. Recent computational and experimental studies revealed that both protein-coding and non-coding RNAs will fold with extensive intramolecular secondary structure, which will result in close distances between the sequence ends. This proximity of the ends is a sequence-independent, universal property of most RNAs. Only low-complexity sequences without guanosines are without secondary structure and exhibit end-to-end distances expected for RNA random coils. The innate proximity of RNA ends might have important biological implications that remain unexplored. In particular, the inherent compactness of mRNA might regulate translation initiation by facilitating the formation of protein complexes that bridge mRNA 5' and 3' ends. Additionally, the proximity of mRNA ends might mediate coupling of 3' deadenylation to 5' end mRNA decay. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems Translation > Translation Regulation.
On Global Stability of Optimal Rearrangement Maps

We study the nonlocal vectorial transport equation $$\partial _ty+ ({\mathbb {P}}y \cdot \nabla ) y=0$$ on bounded domains of $${\mathbb {R}}^d$$ where $${\mathbb {P}}$$ denotes the Leray projector. This equation was introduced to obtain the unique optimal rearrangement of a given map $$y_0$$ as the infinite time limit of the solution with initial data $$y_0$$ (Angenent et al.: SIAM J Math Anal 35:61–97, 2003; McCann: A convexity theory for interacting gases and equilibrium crystals. Thesis (Ph.D.)-Princeton University, ProQuest LLC, Ann Arbor, MI, p 163, 1994; Brenier: J Nonlinear Sci 19(5):547–570, 2009). We rigorously justify this expectation by proving that for initial maps $$y_0$$ sufficiently close to maps with strictly convex potential, the solutions y are global in time and converge exponentially quickly to the optimal rearrangement of $$y_0$$ as time tends to infinity.
Competing Selling Strategies in the Housing Market

Due to the heterogeneous nature of real estate assets, housing market is characterized by a sequential search and seller’s optimal strategies. This paper provides a side-by-side performance comparison of the two competing stopping rules (i.e, the reservation rule and the number rule) in the housing market. It demonstrates that the selling strategies by these two stopping rules are not mutually exclusive, and the optimal choice of the two strategies varies over market conditions. Our analyses reveal clear evidence that no strategy consistently outperforms the other under all market conditions. Therefore, a single selling strategy is likely to result in suboptimal selling outcomes, and the widely-used reservation rule in academic research is likely to result in biased findings. Furthermore, we identify the explicit impact of several supply and demand factors on the optimal trading outcome and provide rich insights into optimal selling strategies in the housing market.
On Squares of Sums of Three Cubes

We show that almost every positive integer can be expressed as a sum of four squares of integers represented as the sums of three positive cubes.
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning.

Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a well-designed reward function is provided, and learn a single behavior for that single reward function. Such reward functions can be difficult to design in practice. Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks? In this paper, we demonstrate that a recently proposed unsupervised skill discovery algorithm can be extended into an efficient off-policy method, making it suitable for performing unsupervised reinforcement learning in the real world. Firstly, we show that our proposed algorithm provides substantial improvement in learning efficiency, making reward-free real-world training feasible. Secondly, we move beyond the simulation environments and evaluate the algorithm on real physical hardware. On quadrupeds, we observe that locomotion skills with diverse gaits and different orientations emerge without any rewards or demonstrations. We also demonstrate that the learned skills can be composed using model predictive control for goal-oriented navigation, without any additional training.
Effectively Prefetching Remote Memory with Leap

Memory disaggregation over RDMA can improve the performance of memory-constrained applications by replacing disk swapping with remote memory accesses. However, state-of-the-art memory disaggregation solutions still use data path components designed for slow disks. As a result, applications experience remote memory access latency significantly higher than that of the underlying low-latency network, which itself is too high for many applications. In this paper, we propose Leap, a prefetching solution for remote memory accesses due to memory disaggregation. At its core, Leap employs an online, majority-based prefetching algorithm, which increases the page cache hit rate. We complement it with a lightweight and efficient data path in the kernel that isolates each application's data path to the disaggregated memory and mitigates latency bottlenecks arising from legacy throughput-optimizing operations. Integration of Leap in the Linux kernel improves the median and tail remote page access latencies of memory-bound applications by up to 104.04x and 22.62x, respectively, over the default data path. This leads to up to 10.16x performance improvements for applications using disaggregated memory in comparison to the state-of-the-art solutions.
Risk stratification of hospitalized COVID-19 patients through comparative studies of laboratory results with influenza

Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) in December 2019 overlaps with the flu season. Methods We compared clinical and laboratory results from 719 influenza and 973 COVID-19 patients from January to April 2020. We compiled laboratory results from the first 14 days of the hospitalized patients using parameters that are most significantly different between COVID-19 and influenza and hierarchically clustered COVID-19 patients. Findings Compared to influenza, patients with COVID-19 exhibited a continued increase in white blood cell count, rapid decline of hemoglobin, more rapid increase in blood urea nitrogen (BUN) and D-dimer, and higher level of alanine transaminase, C-reactive protein, ferritin, and fibrinogen. COVID-19 patients were sub-classified into 5 clusters through a hierarchical clustering analysis. Medical records were reviewed and patients were risk stratified based on the clinical outcomes. The cluster with the highest risk showed 27·8% fatality, 94% ICU admission, 94% intubation, and 28% discharge rates compared to 0%, 38%, 22%, and 88% in the lowest risk cluster, respectively. Patients in the highest risk cluster had leukocytosis including neutrophilia and monocytosis, severe anemia, increased red blood cell distribution width, higher BUN, creatinine, D-dimer, alkaline phosphatase, bilirubin, and troponin. Interpretation There are significant differences in the clinical and laboratory courses between COVID-19 and influenza. Risk stratification in hospitalized COVID-19 patients using laboratory data could be useful to predict clinical outcomes and pathophysiology of these patients. Funding National Institute of Diabetes and Digestive and Kidney Disease, Department of Defense, and National Heart, Lung, and Blood Institute.
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data

Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel principal component analysis (KPCA) for fault detection. We show that sensor data monitoring with low dimensional representations provides better interpretability and is conducive to edge processing in IoT applications.
An LP-based, Strongly Polynomial 2-Approximation Algorithm for Sparse Wasserstein Barycenters

Discrete Wasserstein barycenters correspond to optimal solutions of transportation problems for a set of probability measures with finite support. Discrete barycenters are measures with finite support themselves and exhibit two favorable properties: there always exists one with a provably sparse support, and any optimal transport to the input measures is non-mass splitting. It is open whether a discrete barycenter can be computed in polynomial time. It is possible to find an exact barycenter through linear programming, but these programs may scale exponentially. In this paper, we prove that there is a strongly-polynomial 2-approximation algorithm based on linear programming. First, we show that an exact computation over the union of supports of the input measures gives a tight 2-approximation. This computation can be done through a linear program with setup and solution in strongly-polynomial time. The resulting measure is sparse, but an optimal transport may split mass. We then devise a second, strongly-polynomial algorithm to improve this measure to one with a non-mass splitting transport of lower cost. The key step is an update of the possible support set to resolve mass split. Finally, we devise an iterative scheme that alternates between these two algorithms. The algorithm terminates with a 2-approximation that has both a sparse support and an associated non-mass splitting optimal transport. We conclude with some sample computations and an analysis of the scaling of our algorithms, exhibiting vast improvements in running time over exact LP-based computations and low practical errors.
The Lon Protease Links Nucleotide Metabolism with Proteotoxic Stress

During proteotoxic stress, bacteria maintain critical processes like DNA replication while removing misfolded proteins, which are degraded by the Lon protease. Here, we show that in Caulobacter crescentus Lon controls deoxyribonucleoside triphosphate (dNTP) pools during stress through degradation of the transcription factor CcrM. Elevated dNTP/nucleotide triphosphate (NTP) ratios in Δlon cells protects them from deletion of otherwise essential deoxythymidine triphosphate (dTTP)-producing pathways and shields them from hydroxyurea-induced loss of dNTPs. Increased dNTP production in Δlon results from higher expression of ribonucleotide reductase driven by increased CcrM. We show that misfolded proteins can stabilize CcrM by competing for limited protease and that Lon-dependent control of dNTPs improves fitness during protein misfolding conditions. We propose that linking dNTP production with availability of Lon allows Caulobacter to maintain replication capacity when misfolded protein burden increases, such as during rapid growth. Because Lon recognizes misfolded proteins regardless of the stress, this mechanism allows for response to a variety of unanticipated conditions.
Deductron — a recurrent neural network

The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which requires long-term memory to decode. Our data is a simple writing system, encoding characters 'X' and 'O' as their upper halves, which is possible due to symmetry of the two characters. The characters can be connected, as in some languages using cursive, such as Arabic (abjad). The string 'XOOXXO' may be encoded as '${\vee}{\wedge}\kern-1.5pt{\wedge}{\vee}\kern-1.5pt{\vee}{\wedge}$'. It is clear that seeing a sequence fragment '$|\kern-1.8pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.8pt|$' of any length does not allow us to decode the sequence as '\ldots XXX\ldots' or '\ldots OOO \ldots' due to inherent ambiguity, thus requiring long-term memory. Subsequently we constructed an RNN capable of decoding sequences like this example. Rather than by training, we constructed our RNN ``by inspection'', i.e. we guessed its weights. This involved a sequence of steps. We wrote a conventional program which decodes the sequences as the example above. Subsequently, we interpreted the program as a neural network (the only example of this kind known to us). Finally, we generalized this neural network to discover a new RNN architecture whose instance is our handcrafted RNN. It turns out to be a three-layer network, where the middle layer is capable of performing simple logical inferences; thus the name ``deductron''. It is demonstrated that it is possible to train our network by simulated annealing. Also, known variants of stochastic gradient descent (SGD) methods are shown to work.
Semi-Supervised Speech Emotion Recognition With Ladder Networks

Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lack of generalization across different conditions. For example, systems that show superior performance on certain databases show poor performance when tested on other corpora. This problem can be solved by training models on large amounts of labeled data from the target domain, which is expensive and time-consuming. Another approach is to increase the generalization of the models. An effective way to achieve this goal is by regularizing the models through multitask learning (MTL), where auxiliary tasks are learned along with the primary task. These methods often require the use of labeled data which is computationally expensive to collect for emotion recognition (gender, speaker identity, age or other emotional descriptors). This study proposes the use of ladder networks for emotion recognition, which utilizes an unsupervised auxiliary task. The primary task is a regression problem to predict emotional attributes. The auxiliary task is the reconstruction of intermediate feature representations using a denoising autoencoder. This auxiliary task does not require labels so it is possible to train the framework in a semi-supervised fashion with abundant unlabeled data from the target domain. This study shows that the proposed approach creates a powerful framework for SER, achieving superior performance than fully supervised single-task learning (STL) and MTL baselines. We implement the approach with sentence-level or frame-level features, demonstrating the flexibility of our approach. Additionally, the generalization of the ladder networks is evaluated in cross-corpus settings using sentence-level features, obtaining important improvements. Compared to the STL baselines, the proposed approach achieves relative gains in concordance correlation coefficient (CCC) between 3.0% and 3.5% for within corpus evaluations, and between 16.1% and 74.1% for cross corpus evaluations, highlighting the power of the architecture.
Dispossessing Resident Voice: Municipal Receiverships and the Public Trust

The residents of struggling cities suffer property dispossessions both as individual owners and as municipal residents. Their individual dispossessions are part of a cycle that often begins with industrial decline. In Detroit, for example, more than 100,000 residents have lost their homes to tax foreclosure over a four-year period that bracketed the city’s bankruptcy filing. Falling property values, job losses, and foreclosures affect municipal budgets by reducing tax revenues. As individual dispossessions exacerbate municipal financial crises, residents can also face the loss of municipal property. Struggling cities and towns often sell publicly owned property—from parks to parking systems—to balance municipal budgets. This article discusses the relationship between property dispossessions and proceedings to resolve municipal financial distress, with a focus on another important loss faced by residents of distressed municipalities—the loss of their voice in municipal government. A municipal financial crisis, by itself, has no effect on the property of any individuals who live in the city, and a city’s bankruptcy does not take a city’s assets in the same way that a corporate or personal bankruptcy can take the property of a business or individual. Yet even though creditors cannot force the sale of city-owned assets, the decision to transfer the property may be made by unelected officials appointed by the state government to replace city government in times of financial crisis. This results in another type of collective dispossession—the dispossession of resident voice in local government affairs. This article discusses how insolvency proceedings, including Chapter 9 bankruptcy, can deprive residents of their voice and, in turn, deprive them of the city’s assets that the city holds for them in public trust and proposes some suggestions for states for balancing the need for resident voice with higher-level financial oversight as they determine how to manage the financial distress of their cities.
Ceramide-Enriched Membrane Domains Contribute to Targeted and Nontargeted Effects of Radiation through Modulation of PI3K/AKT Signaling in HNSCC Cells

We investigated the potential involvement of ceramide-enriched membrane domains in radiation-induced targeted and nontargeted effects using head and neck squamous cell carcinoma with opposite radiosensitivities. In radiosensitive SCC61 cells, the proportion of targeted effects was 34% and nontargeted effects killed 32% of cells. In contrast, only targeted effects (30%) are involved in the overall death of radioresistant SQ20B cells. We then demonstrated in SCC61 cells that nontargeted cell response was driven by the formation of the radiation-induced ceramide-enriched domain. By contrast, the existence of these platforms in SQ20B cells confers a permissive region for phosphatidylinositol-3-kinase (PI3K)/AKT activation. The disruption of lipid raft results in strong inhibition of PI3K/AKT signaling, leading to radiosensitization and apparition of nontargeted effects. These results suggest that ceramide-enriched platforms play a significant role in targeted and nontargeted effects during radiotherapy and that drugs modulating cholesterol levels may be a good alternative for improving radiotherapy effectiveness.
Sparse Interpolation With Errors in Chebyshev Basis Beyond Redundant-Block Decoding

We present sparse interpolation algorithms for recovering a polynomial with $\le B$ terms from $N$ evaluations at distinct values for the variable when $\le E$ of the evaluations can be erroneous. Our algorithms perform exact arithmetic in the field of scalars $ \mathsf {K}$ and the terms can be standard powers of the variable or Chebyshev polynomials, in which case the characteristic of $ \mathsf {K}$ is $\ne 2$ . Our algorithms return a list of valid sparse interpolants for the $N$ support points and run in polynomial-time. For standard power basis our algorithms sample at $N = \lfloor \frac {4}{3} E + 2 \rfloor B$ points, which are fewer points than $N = 2(E+1)B - 1$ given by Kaltofen and Pernet in 2014. For Chebyshev basis our algorithms sample at $N = \lfloor \frac {3}{2} E + 2 \rfloor B$ points, which are also fewer than the number of points required by the algorithm given by Arnold and Kaltofen in 2015, which has $N = 74 \lfloor \frac {E}{13} + 1 \rfloor $ for $B = 3$ and $E \ge 222$ . Our method shows how to correct 2 errors in a block of $4B$ points for standard basis and how to correct 1 error in a block of $3B$ points for Chebyshev Basis.
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

In many practical settings, the decision makers have to learn their best actions by experimenting with possible options and collecting feedback (data) over time. It is often assumed that the collec...
Predicting U.S. Recessions: Financial Variables as Leading Indicators

This paper examines the out-of-sample performance of various financial variables as predictors of U.S. recessions. Series such as interest rates and spreads, stock prices, and monetary aggregates are evaluated individually and in comparison with other financial and nonfinancial indicators. The analysis focuses on out-of-sample performance from one to eight quarters ahead. Results show that stock prices are useful with one- to three-quarter horizons, as are some well-known macroeconomic indicators. Beyond one quarter, however, the slope of the yield curve emerges as the clear individual choice and typically performs better by itself out of sample than in conjunction with other variables.
PTHrP targets salt-inducible kinases, HDAC4 and HDAC5, to repress chondrocyte hypertrophy in the growth plate

Hypertrophy of chondrocytes is a crucial step in the endochondral bone formation process that drives bone lengthening and the transition to endochondral bone formation. Both Parathyroid hormone-related protein (PTHrP) and Histone deacetylase 4 (HDAC4) inhibit chondrocyte hypertrophy. Use of multiple mouse genetics models reveals how PTHrP and HDAC4 participate in a pathway that regulates chondrocyte hypertrophy. PTHrP/cAMP/protein kinase A (PKA) signaling pathway phosphorylates the PKA-target sites on salt-inducible kinase 3 (Sik3), which leads to inhibition of Sik3 kinase activity. Inhibition of Sik3 kinase activity decreases phosphorylation of HDAC4 by Sik3 at binding sites for 14-3-3; lower levels of HDAC4 phosphorylation then allow HDAC4 nuclear translocation. In the nucleus, the transcription factor, Myocyte Enhancer Factor 2 (Mef2), activates Runt-related transcription factor 2 (Runx2), and together these two transcription factors drive the hypertrophic process. HDAC4 binds both Mef2 and Runx2 and blocks their activities. There are genetic redundancies in this pathway. Sik1 and Sik2 also mediate PTHrP/cAMP/PKA signaling when Sik3 activity is low. HDAC5 also mediates PTHrP signaling when HDAC4 expression is low. Thus, PTHrP triggers a kinase cascade that leads to inhibition of the key transcription factors (Mef2 and Runx2) that promote chondrocyte hypertrophy.
Loving Others: The Impact of Compassionate Love on Later-Life Psychological Well-being

OBJECTIVES Existing scholarship in social gerontology has paid relatively little attention to broader loving emotions, such as compassionate and altruistic love, as potentially meaningful mechanisms for improving later-life psychological well-being outside a family framework. METHOD Drawing from a 3-wave longitudinal survey of community-dwelling older residents (n = 334) of Miami, Florida, we utilized generalized estimating equation models to examine the influence of changes in compassionate love (i.e., feeling love toward other persons and experiencing love from others) on depressive symptoms over time. We also explored cross-sectional relationship between compassionate love and positive and negative affects. RESULTS An increase in the feeling of being loved (β = -0.77, p < .001) and feeling love for others (β = -0.78, p < .001) led to a decline in odds of reporting greater levels of depressive symptoms over time. The odds of reporting higher level of positive affect were significantly greater for older adults who reported feeling loved by others (β = .63, p < .001) and expressed love for other people (β = 0.43, p < .05). Older adults who felt loved and expressed love for other people, respectively, had 0.71 and 0.54-point lower ordered log odds of reporting higher negative affect than those who reported lower levels of love. The statistically significant impact of feeling loved on all well-being outcomes was maintained even after adjustment for altruistic attitudes and emotional support. Except for depressive symptoms, such adjustments explained the positive influence of love for others on well-being outcomes. DISCUSSION Our findings underscore the powerful influence of both receiving and giving loving emotions for the maintenance of later-life psychological well-being.
MOMENTUM EXPANSION OF MASSIVE TWO-LOOP FEYNMAN GRAPHS AROUND A FINITE VALUE

We give an algorithm for obtaining expansions of massive two-loop Feynman graphs in powers of the external momentum around a finite, nonzero value of the momentum. Such momentum derivatives are encountered while evaluating physical radiative corrections, such as in wave function renormalization constants. This is based on our general two-loop formalism to reduce massive two-loop graphs with renormalizable interactions into a standard set of special functions. After the algebraic reduction, the final results are obtained by numerical integration. We apply the expansion algorithm to treat the top-dependent corrections of to the b-quark self-energy and extract its momentum expansion on-shell.
On Collisions Times of ‘Self-Sorting’ Interacting Particles in One-Dimension with Random Initial Positions and Velocities

We investigate a one-dimensional system of N particles, initially distributed with random positions and velocities, interacting through binary collisions. The collision rule is such that there is a time after which the N particles do not interact and become sorted according to their velocities. When the collisions are elastic, we derive asymptotic distributions for the final collision time of a single particle and the final collision time of the system as the number of particles approaches infinity, under different assumptions for the initial distributions of the particles’ positions and velocities. For comparison, a numerical investigation is carried out to determine how a non-elastic collision rule, which conserves neither momentum nor energy, affects the median collision time of a particle and the median final collision time of the system.
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1 mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.
Gallery of Planetary Nebula Spectra

We present the Gallery of Planetary Nebula Spectra now available at this http URL The website offers high-quality, moderate resolution (~7-10 A FWHM) spectra of 128 Galactic planetary nebulae from 3600-9600 A, obtained by Kwitter, Henry, and colleagues with the Goldcam spectrograph at the KPNO 2.1-m or with the RC spectrograph at the CTIO 1.5-m. The master PN table contains atlas data and an image link. A selected object's spectrum is displayed in a zoomable window; line identification templates are provided. In addition to the spectra themselves, the website also contains a brief discussion of PNe as astronomical objects and as contributors to our understanding of stellar evolution. We envision that this website, which concentrates a large amount of data in one place, will be of interest to a variety of users: researchers might need to check the spectrum of a particular object of interest; the non-specialist astronomer might simply be interested in perusing such a collection of spectra; and finally, teachers of introductory astronomy can use this database to illustrate basic principles of atomic physics and radiation. To particularly encourage this last use, we have developed two paper-and-pencil exercises to introduce beginning astronomy students to the wealth of information that PN spectra contain.
Newtonian Fractional-Dimension Gravity and MOND

This paper introduces a possible alternative model of gravity based on the theory of fractional-dimension spaces and its applications to Newtonian gravity. In particular, Gauss’s law for gravity as well as other fundamental classical laws are extended to a D-dimensional metric space, where D can be a non-integer dimension. We show a possible connection between this Newtonian Fractional-Dimension Gravity (NFDG) and Modified Newtonian Dynamics (MOND), a leading alternative gravity model which accounts for the observed properties of galaxies and other astrophysical structures without requiring the dark matter hypothesis. The MOND acceleration constant $${a_{0}} \simeq 1.2 \times 10^{-10} \text {m}\, \text{s}^{-2}$$ can be related to a natural scale length $$l_{0}$$ in NFDG, i.e., $$a_{0} \approx GM/l_{0}^{2}$$ , for astrophysical structures of mass M, and the deep-MOND regime is present in regions of space where the dimension is reduced to $$D \approx 2$$ . For several fundamental spherically-symmetric structures, we compare MOND results, such as the empirical Radial Acceleration Relation (RAR), circular speed plots, and logarithmic plots of the observed radial acceleration $$g_{obs}$$ vs. the baryonic radial acceleration $$g_{bar}$$ , with NFDG results. We show that our model is capable of reproducing these results using a variable local dimension $$D\left( w\right)$$ , where $$w =r/l_{0}$$ is a dimensionless radial coordinate. At the moment, we are unable to derive explicitly this dimension function $$D\left( w\right)$$ from first principles, but it can be obtained empirically in each case from the general RAR. Additional work on the subject, including studies of axially-symmetric structures, detailed galactic rotation curves fitting, and a possible relativistic extension, will be needed to establish NFDG as a viable alternative model of gravity.
Conformal enhancement of holographic scaling in black hole thermodynamics: a near-horizon heat-kernel framework

Standard thermodynamic treatments of quantum field theory in the presence of black-hole backgrounds reproduce the black hole entropy by usually specializing to the leading order of the heat-kernel or the high-temperature expansion. By contrast, this work develops a hybrid framework centered on geometric spectral asymptotics whereby these assumptions are shown to be unwarranted insofar as black hole thermodynamics is concerned. The approach—consisting of the concurrent use of near-horizon and heat-kernel asymptotic expansions—leads to a proof of the holographic scaling of the entropy as a universal feature driven by conformal quantum mechanics.
Spin switches for compact implementation of neuron and synapse

Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltages that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network.
GALAXY MERGERS AS A SOURCE OF COSMIC RAYS, NEUTRINOS, AND GAMMA RAYS

We investigate the shock acceleration of particles in massive galaxy mergers or collisions, and show that cosmic rays (CRs) can be accelerated up to the second knee energy ~0.1-1 EeV and possibly beyond, with a hard spectral index of Γ ≈ 2. Such CRs lose their energy via hadronuclear interactions within a dynamical timescale of the merger shock, producing gamma rays and neutrinos as a by-product. If ~10% of the shock dissipated energy goes into CR acceleration, some local merging galaxies will produce gamma-ray counterparts detectable by the Cherenkov Telescope Array. Also, based on the concordance cosmology, where a good fraction of the massive galaxies experience a major merger in a cosmological timescale, the neutrino counterparts can constitute ~20%-60% of the isotropic background detected by IceCube.
Adiabatic Perturbation Theory: From Landau–Zener Problem to Quenching Through a Quantum Critical Point

Dynamics in closed systems recently attracted a lot of theoretical interest largely following experimental developments in cold atom systems (see e.g., [1] for a review). Several spectacular experiments already explored different aspects of non-equilibrium dynamics in interacting many-particle systems [2–8]. Recent theoretical works in this context focused on various topics, for instance: connection of dynamics and thermodynamics [9–11 M. Rigol, unpublished], dynamics following a sudden quench in low dimensional systems [11–23, L. Mathey and A. Polkovnikov, unpublished; A. Iucci and M.A. Cazalilla,unpublished], adiabatic dynamics near quantum critical points [24–37, D. Chowdhury et al., unpublished; K. Sengupta and D. Sen, unpublished; A.P. Itin and P. Torma, unpublished; F. Pollmann et al., unpublished] and others. Though there is still very limited understanding of the generic aspects of non-equilibrium quantum dynamics, it has been recognized that such issues as integrability, dimensionality, universality (near critical points) can be explored to understand the non-equilibrium behavior of many-particle systems in various specific situations.
Quantitative null-cobordism

For a given null-cobordant Riemannian n n -manifold, how does the minimal geometric complexity of a null-cobordism depend on the geometric complexity of the manifold? Gromov has conjectured that this dependence should be linear. We show that it is at most a polynomial whose degree depends on n n . In the appendix the bound is improved to one that is O ( L 1 + e ) O(L^{1+\varepsilon }) for every e > 0 \varepsilon >0 . This construction relies on another of independent interest. Take X X and Y Y to be sufficiently nice compact metric spaces, such as Riemannian manifolds or simplicial complexes. Suppose Y Y is simply connected and rationally homotopy equivalent to a product of Eilenberg–MacLane spaces, for example, any simply connected Lie group. Then two homotopic L L -Lipschitz maps f , g : X → Y f,g:X \to Y are homotopic via a C L CL -Lipschitz homotopy. We present a counterexample to show that this is not true for larger classes of spaces Y Y .
Topological Insulators from Group Cohomology

Naturally occurring crystals are classified by spacetime symmetries, and now researchers theoretically expand this classification, with the aim of topologically classifying band insulators.
A Gibbons–Penrose Inequality for Surfaces in Schwarzschild Spacetime

We propose a geometric inequality for two-dimensional spacelike surfaces in the Schwarzschild spacetime. This inequality implies the Penrose inequality for collapsing dust shells in general relativity, as proposed by Penrose and Gibbons. We prove that the inequality holds in several important cases.
Disk Diffusion Propagation Model for the Outburst of XTE J1118+480

We present a linear diffusion model for the evolution of the double-peaked outburst in the transient source XTE J1118+480. The model treats the two outbursts as episodic mass deposition at the outer radius of the disk followed by evolution of disk structure according to a diffusion process. We demonstrate that light curves with fast-rise, exponential decay profile are a general consequence of the diffusion process. Deconvolution of the light curve proves to be feasible and gives an input function specifying mass deposition at the outer disk edge as well as the total mass of the disk, both as functions of time. The derived evolution of total disk mass can be correlated with the observed evolution of the ~0.1 Hz QPO in the source reported by Wood et al.
Black Holes from the Dark Ages:. Exploring the Reionization ERA and Early Structure Formation with Quasars and Gamma-Ray Bursts

The cosmic reionization era, which includes formation of the first stars, galaxies, and AGN, is now one of the most active frontiers of cosmological research. We review briefly our current understanding of the early structure formation, and use the ideas about a joint formation of massive black holes (which power the early QSOs) and their host galaxies to employ high-redshift QSOs as probes of the early galaxy formation and primordial large-scale structure. There is a growing evidence for a strong biasing in the formation of the first luminous sources, which would lead to a clumpy reionization. Absorption spectroscopy of QSOs at z > 6 indicates the end of the reionization era at z ~ 6; yet measurements from the WMAP satellite suggest and early reionization at z ~ 10 — 20. The first generation of massive stars, perhaps aided by the early mini-quasars, may have reionized the universe at such high redshifts, but their feedback may have disrupted the subsequent star and galaxy formation, leading to an extended and perhaps multimodal reionization history ending by z ~ 6. Observations of 7-ray bursts from the death events of these putative Population III stars may provide essential insight into the primordial structure formation, reionization, early chemical enrichment, and formation of seed black holes which may grow to become central engines of luminous quasars.
The global existence of small self-interacting scalar field propagating in the contracting universe

We present a condition on the self-interaction term that guaranties the existence of the global in time solution of the Cauchy problem for the semilinear Klein–Gordon equation in the Friedmann–Lamaitre–Robertson–Walker model of the contracting universe. For the Klein–Gordon equation with the Higgs potential we give a lower estimate for the lifespan of solution.
Resonances in the Photoionization Cross Sections of Atomic Nitrogen Shape the Far-ultraviolet Spectrum of the Bright Star in 47 Tucanae

The far-ultraviolet spectrum of the Bright Star (B8 III) in 47 Tuc (NGC 104) shows a remarkable pattern: it is well fit by local thermodynamic equilibrium models at wavelengths longer than Lyβ, but at shorter wavelengths it is fainter than the models by a factor of two. A spectrum of this star obtained with the Far Ultraviolet Spectroscopic Explorer shows broad absorption troughs with sharp edges at 995 and 1010 A and a deep absorption feature at 1072 A; none of which are predicted by the models. We find that these features are caused by resonances in the photoionization cross sections of the first and second excited states of atomic nitrogen (2s 2 2p 3 2 D 0 and 2 P 0). Using cross sections from the Opacity Project, we can reproduce these features, but only if we use the cross sections at their full resolution, rather than the resonance-averaged cross sections usually employed to model stellar atmospheres. These resonances are strongest in stellar atmospheres with enhanced nitrogen and depleted carbon abundances, a pattern typical of post-asymptotic giant branch stars.