cssi/SciSciGPT-SciSciNet huggingface dataset:
Joint seasonality in geographic and ecological spaces, illustrated with a partially migratory bird

As most species live in seasonal environments, considering varying conditions is essential to understand species dynamics in both geographic and ecological spaces. Both resident and migratory species need to contend with seasonality, and balance settling in favorable areas with tracking favorable environmental conditions during the year. We present an exploratory framework to jointly investigate a species9 niche in geographic and ecological spaces, applied to wood storks (Mycteria americana), which are partially migratory wading birds, in the southeastern U.S. We concurrently described monthly geographic distributions and climatic niches based on temperature and precipitation. Geographic distributions of wood storks were more similar throughout the year than were climatic niches, suggesting that birds stay within specific areas seasonally, rather than tracking areas of similar climate. However, wood storks expressed consistent selection of warm areas during the winter, and wet areas during the summer, indicating that the selection of seasonal ranges may be directly related to environmental conditions across the entire range. Our flexible framework, which simultaneously considered geographic and ecological spaces, suggested that tracking climate alone did not explain seasonal distributions of wood storks in breeding and non-breeding areas.
Volumetric Landmark Detection with a Multi-Scale Shift Equivariant Neural Network

Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations, which in turn can limit the precision of the output. We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. Our architecture consists of blocks of shift-equivariant networks, each of which performs landmark detection at a different spatial scale. These blocks are connected from coarse to fine-scale, with differentiable resampling layers, so that all levels can be trained together. We also present a noise injection strategy that increases the robustness of the model and allows us to quantify uncertainty at test time. We evaluate our method for carotid artery bifurcations detection on 263 CT volumes and achieve a better than state-of-the-art accuracy with mean Euclidean distance error of 2.81mm.
Formalizing Data Deletion in the Context of the Right to Be Forgotten

The right of an individual to request the deletion of their personal data by an entity that might be storing it – referred to as the right to be forgotten – has been explicitly recognized, legislated, and exercised in several jurisdictions across the world, including the European Union, Argentina, and California. However, much of the discussion surrounding this right offers only an intuitive notion of what it means for it to be fulfilled – of what it means for such personal data to be deleted.
Optimal Bidding Strategy without Exploration in Real-time Bidding

Maximizing utility with a budget constraint is the primary goal for advertisers in real-time bidding (RTB) systems. The policy maximizing the utility is referred to as the optimal bidding strategy. Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint. Further, the advertiser observes a censored market price which makes direct evaluation infeasible on batch test datasets. Previous works ignore the losing auctions to alleviate the difficulty with censored states; thus significantly modifying the test distribution. We address the challenge of lacking a clear evaluation procedure as well as the error propagated through batch reinforcement learning methods in RTB systems. We exploit two conditional independence structures in the sequential bidding process that allow us to propose a novel practical framework using the maximum entropy principle to imitate the behavior of the true distribution observed in real-time traffic. Moreover, the framework allows us to train a model that can generalize to the unseen budget conditions than limit only to those observed in history. We compare our methods on two real-world RTB datasets with several baselines and demonstrate significantly improved performance under various budget settings.
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the state-action space is large. A common practice is to parameterize the high-dimensional value and policy functions using given features. However existing methods either have no theoretical guarantee or suffer a regret that is exponential in the planning horizon $H$. In this paper, we propose an online RL algorithm, namely the MatrixRL, that leverages ideas from linear bandit to learn a low-dimensional representation of the probability transition model while carefully balancing the exploitation-exploration tradeoff. We show that MatrixRL achieves a regret bound ${O}\big(H^2d\log T\sqrt{T}\big)$ where $d$ is the number of features. MatrixRL has an equivalent kernelized version, which is able to work with an arbitrary kernel Hilbert space without using explicit features. In this case, the kernelized MatrixRL satisfies a regret bound ${O}\big(H^2\widetilde{d}\log T\sqrt{T}\big)$, where $\widetilde{d}$ is the effective dimension of the kernel space. To our best knowledge, for RL using features or kernels, our results are the first regret bounds that are near-optimal in time $T$ and dimension $d$ (or $\widetilde{d}$) and polynomial in the planning horizon $H$.
(Individual) Fairness for k-Clustering

We give a local search based algorithm for $k$-median and $k$-means (and more generally for any $k$-clustering with $\ell_p$ norm cost function) from the perspective of individual fairness. More precisely, for a point $x$ in a point set $P$ of size $n$, let $r(x)$ be the minimum radius such that the ball of radius $r(x)$ centered at $x$ has at least $n/k$ points from $P$. Intuitively, if a set of $k$ random points are chosen from $P$ as centers, every point $x\in P$ expects to have a center within radius $r(x)$. An individually fair clustering provides such a guarantee for every point $x\in P$. This notion of fairness was introduced in [Jung et al., 2019] where they showed how to get an approximately feasible $k$-clustering with respect to this fairness condition. In this work, we show how to get a bicriteria approximation for fair $k$-clustering: The $k$-median ($k$-means) cost of our solution is within a constant factor of the cost of an optimal fair $k$-clustering, and our solution approximately satisfies the fairness condition (also within a constant factor). Further, we complement our theoretical bounds with empirical evaluation.
Penalized semidefinite programming for quadratically-constrained quadratic optimization

In this paper, we give a new penalized semidefinite programming approach for non-convex quadratically-constrained quadratic programs (QCQPs). We incorporate penalty terms into the objective of convex relaxations in order to retrieve feasible and near-optimal solutions for non-convex QCQPs. We introduce a generalized linear independence constraint qualification (GLICQ) criterion and prove that any GLICQ regular point that is sufficiently close to the feasible set can be used to construct an appropriate penalty term and recover a feasible solution. Inspired by these results, we develop a heuristic sequential procedure that preserves feasibility and aims to improve the objective value at each iteration. Numerical experiments on large-scale system identification problems as well as benchmark instances from the library of quadratic programming demonstrate the ability of the proposed penalized semidefinite programs in finding near-optimal solutions for non-convex QCQP.
Intensity-based estimation of extreme loss event probability and value at risk

We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self-exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.
Vecchia Approximations of Gaussian-Process Predictions

Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasible for large datasets. Vecchia approximations of GPs have been used to enable fast evaluation of the likelihood for parameter inference. Here, we study Vecchia approximations of spatial predictions at observed and unobserved locations, including obtaining joint predictive distributions at large sets of locations. We consider a general Vecchia framework for GP predictions, which contains some novel and some existing special cases. We study the accuracy and computational properties of these approaches theoretically and numerically, proving that our new methods exhibit linear computational complexity in the total number of spatial locations. We show that certain choices within the framework can have a strong effect on uncertainty quantification and computational cost, which leads to specific recommendations on which methods are most suitable for various settings. We also apply our methods to a satellite dataset of chlorophyll fluorescence, showing that the new methods are faster or more accurate than existing methods and reduce unrealistic artifacts in prediction maps. Supplementary materials accompanying this paper appear on-line.
Benign Hepatic Nodules Mimicking Hepatocellular Carcinoma in the Setting of Fontan-associated Liver Disease: A Case Report.

Fontan procedure, in which systemic circulation is redirected into pulmonary circulation by a baffle, is a palliative surgical strategy for patients born with single ventricle congenital heart disease. Hemodynamic changes secondary to Fontan procedure, also termed as Fontan physiology, result in end-organ damage, especially of the liver. Fontan-associated liver disease (FALD) represents a spectrum of pathologies ranging from mild liver fibrosis to advanced liver cirrhosis and hepatocellular carcinoma (HCC). Hepatic nodules, some of which have been documented as HCC in several case series and reports, are a recognized complicated feature of FALD. Herein, we report a case with benign hepatic nodules mimicking HCC by imaging characteristics, emphasizing the fact that arterially enhancing lesions with delayed washout appearance may reflect benign regenerative or focal nodular hyperplasia-like nodules in patients with Fontan physiology. How to cite this article Colaklar A, Lehnert SJ, Tirkes T. Benign Hepatic Nodules Mimicking Hepatocellular Carcinoma in the Setting of Fontan-associated Liver Disease: A Case Report. Euroasian J Hepato-Gastroenterol 2020;10(1):42-44.
A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees

Abstract Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. Related studies have shown that neural networks and support vector machines outperform other techniques by providing better prediction accuracy. The purpose of this paper is two fold. First, we provide a survey and a comparative analysis of results from literature applying machine learning techniques to predict credit rating. Second, we apply ourselves four machine learning techniques deemed useful from previous studies (Bagged Decision Trees, Random Forest, support vector machine and Multilayer Perceptron) to the same datasets. We evaluate the results using a 10-fold cross validation technique. The results of the experiment for the datasets chosen show superior performance for decision tree based models. In addition to the conventional accuracy measure of classifiers, we introduce a measure of accuracy based on notches called ”Notch Distance” to analyze the performance of the above classifiers in the specific context of credit rating. This measure tells us how far the predictions are from the true ratings. We further compare the performance of three major rating agencies, Standard & Poors, Moody’s and Fitch where we show that the difference in their ratings is comparable with the decision tree prediction versus the actual rating on the test dataset.
Colonization of the Murine Oropharynx by Streptococcus pyogenes Is Governed by the Rgg2/3 Quorum Sensing System.

ABSTRACT Streptococcus pyogenes is a human-restricted pathogen most often found in the human nasopharynx. Multiple bacterial factors are known to contribute to persistent colonization of this niche, and many are important in mucosal immunity and vaccine development. In this work, mice were infected intranasally with transcriptional regulator mutants of the Rgg2/3 quorum sensing (QS) system—a peptide-based signaling system conserved in sequenced isolates of S. pyogenes. Deletion of the QS system’s transcriptional activator (Δrgg2) dramatically diminished the percentage of colonized mice, while deletion of the transcriptional repressor (Δrgg3) increased the percentage of colonized mice compared to that of the wild type (WT). Stimulation of the QS system using synthetic pheromones prior to inoculation did not significantly increase the percentage of animals colonized, indicating that QS-dependent colonization is responsive to the intrinsic conditions within the host upper respiratory tract. Bacterial RNA extracted directly from oropharyngeal swabs and evaluated by quantitative reverse transcription-PCR (qRT-PCR) subsequently confirmed QS upregulation within 1 h of inoculation. In the nasal-associated lymphoid tissue (NALT), a muted inflammatory response to the Δrgg2 bacteria suggests that their rapid elimination failed to elicit the previously characterized response to intranasal inoculation of GAS. This work identifies a new transcriptional regulatory system governing the ability of S. pyogenes to colonize the nasopharynx and provides knowledge that could help lead to decolonization therapeutics.
A Semiparametric Method for Assessing Life Expectancy Evaluations

In the life settlements industry, life expectancy (LE) providers are firms that conduct health underwriting toward predicting the future mortality of an insured. Multiple stakeholders are intereste...
Intergenerational Altruism and Retirement Transfers: Evidence from the Social Security Notch

I contribute new evidence on altruistic preferences in intergenerational transfers using variation in Social Security benefits induced by an inflation-indexing mistake. The instrument is most relevant for those with low education, so I focus on this group. I find support for pure altruism because individuals who received additional Social Security benefits passed on 15.4 percent to children via inter vivos transfers without receiving any additional care in return. On the contrary, children reduce caregiving monotonically with increases in parental Social Security benefits. Adult female children appear to be the most affected as they both receive monetary transfers and reduce caregiving.
Quantum B-algebras with involutions

The aim of this paper is to define and study the involutive and weakly involutive quantum B-algebras. We prove that any weakly involutive quantum B-algebra is a residuated poset. As an application,...
ComHapDet: a spatial community detection algorithm for haplotype assembly

Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual’s susceptibility to hereditary and complex diseases and affect how our bodies respond to therapeutic drugs. Reconstructing haplotypes of an individual from short sequencing reads is an NP-hard problem that becomes even more challenging in the case of polyploids. While increasing lengths of sequencing reads and insert sizes helps improve accuracy of reconstruction, it also exacerbates computational complexity of the haplotype assembly task. This has motivated the pursuit of algorithmic frameworks capable of accurate yet efficient assembly of haplotypes from high-throughput sequencing data. We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph. To this end, we construct a graph where each read is a node with an unknown community label associating the read with the haplotype it samples. Haplotype reconstruction can then be thought of as a two-step procedure: first, one recovers the community labels on the nodes (i.e., the reads), and then uses the estimated labels to assemble the haplotypes. Based on this observation, we propose ComHapDet – a novel assembly algorithm for diploid and ployploid haplotypes which allows both bialleleic and multi-allelic variants. Performance of the proposed algorithm is benchmarked on simulated as well as experimental data obtained by sequencing Chromosome 5 of tetraploid biallelic Solanum-Tuberosum (Potato). The results demonstrate the efficacy of the proposed method and that it compares favorably with the existing techniques.
Mode of targeting to the proteasome determines GFP fate

The ubiquitin–proteasome system is the canonical pathway for protein degradation in eukaryotic cells. GFP is frequently used as a reporter in proteasomal degradation assays. However, there are multiple variants of GFP in use, and these variants have different intrinsic stabilities. Further, there are multiple means by which substrates are targeted to the proteasome, and these differences could also affect the proteasome's ability to unfold and degrade substrates. Herein we investigate how the fate of GFP variants of differing intrinsic stabilities is determined by the mode of targeting to the proteasome. We compared two targeting systems: linear Ub4 degrons and the UBL domain from yeast Rad23, both of which are commonly used in degradation experiments. Surprisingly, the UBL degron allows for degradation of the most stable sGFP-containing substrates, whereas the Ub4 degron does not. Destabilizing the GFP by circular permutation allows degradation with either targeting signal, indicating that domain stability and mode of targeting combine to determine substrate fate. Difficult-to-unfold substrates are released and re-engaged multiple times, with removal of the degradation initiation region providing an alternative clipping pathway that precludes unfolding and degradation; the UBL degron favors degradation of even difficult-to-unfold substrates, whereas the Ub4 degron favors clipping. Finally, we show that the ubiquitin receptor Rpn13 is primarily responsible for the enhanced ability of the proteasome to degrade stable UBL-tagged substrates. Our results indicate that the choice of targeting method and reporter protein are critical to the design of protein degradation experiments.
Global Transitional Justice Norms and the Framing of Truth Commissions in the Absence of Transition

As global transitional justice norms strengthen, governments face increasingly pressure to enact formal transitional justice mechanisms to resolve domestic conflict. This article examines how Bahrain, Morocco, and Sri Lanka attempted to exploit these norms to appease demands and stave off international transitional justice intervention by employing truth commissions. Governments framed truth commissions and their responses to their investigations as sufficient to address the past. To varying degrees, though, domestic audiences and the international community refused to accept this framing. As such, truth commission investigations and their reports failed to resolve the respective conflicts. Rather, they prolonged attention on governments’ past and present misdeeds. For governments, the risk this strategy backfires is higher when human rights violations have been more extensive and extreme, when governments construct a more obviously biased truth‐seeking process and display little interest in enacting recommendations, and when governments have failed to cultivate strong ties with Western powers.
Strict Inequality for the Chemical Distance Exponent in Two‐Dimensional Critical Percolation

We provide the first nontrivial upper bound for the chemical distance exponent in two-dimensional critical percolation. Specifically, we prove that the expected length of the shortest horizontal crossing path of a box of side length $n$ in critical percolation on $\mathbb{Z}^2$ is bounded by $Cn^{2-\delta}\pi_3(n)$, for some $\delta>0$, where $\pi_3(n)$ is the "three-arm probability to distance $n$." This implies that the ratio of this length to the length of the lowest crossing is bounded by an inverse power of $n$ with high probability. In the case of site percolation on the triangular lattice, we obtain a strict upper bound for the exponent of $4/3$. The proof builds on the strategy developed in our previous paper, but with a new iterative scheme, and a new large deviation inequality for events in annuli conditional on arm events, which may be of independent interest.
Identification of Orbital Eclipses in LAMOST J024048.51+195226.9, a Candidate AE Aqr-type Cataclysmic Variable Star

AE Aqr objects are a class of cataclysmic variable stars in which the rapidly rotating magnetosphere of the white dwarf (WD) primary centrifugally expels most infalling gas before it can accrete onto the WD. The expulsion of the accretion flow via this "magnetic propeller" extracts angular momentum from the WD and produces large-amplitude, aperiodic flares in optical photometry. The eponymous AE Aqr is the only confirmed member of this class of object, but recently, Thorstensen discovered a candidate AE Aqr system: LAMOST J024048.51+195226.9. Using survey photometry, we measure a refined orbital period for this system and identify a shallow, previously unrecognized eclipse during which the system's frequent AE Aqr-like flaring episodes cease. A dedicated follow-up study is still necessary to test the proposed AE Aqr classification for LAMOST J024048.51+195226.9, but should it be confirmed, the eclipse of its flare-production region will offer a new means of studying the magnetic propeller phenomenon.
Captured by an algorithm

Captured by an Algorithm is a commemorative plate series that looks at romance novels through the lens of Amazon Kindle Popular Highlights. A passage in a Kindle e-book becomes a Popular Highlight after a certain number of people independently highlight the same passage. Popular Highlights are displayed as underlined along with the number of times each has been highlighted by a reader. The highlights in romances are not the racy, salacious quotes one might expect. Instead, they reveal the readers' intense feelings of loneliness, grief, and discontent. With all the social technologies available today, it is astonishing to see that so many people feel so lonely.
Walking Through Doors Is Hard, Even Without Staircases: Proving PSPACE-Hardness via Planar Assemblies of Door Gadgets.

A door gadget has two states and three tunnels that can be traversed by an agent (player, robot, etc.): the "open" and "close" tunnel sets the gadget's state to open and closed, respectively, while the "traverse" tunnel can be traversed if and only if the door is in the open state. We prove that it is PSPACE-complete to decide whether an agent can move from one location to another through a planar assembly of such door gadgets, removing the traditional need for crossover gadgets and thereby simplifying past PSPACE-hardness proofs of Lemmings and Nintendo games Super Mario Bros., Legend of Zelda, and Donkey Kong Country. Our result holds in all but one of the possible local planar embedding of the open, close, and traverse tunnels within a door gadget; in the one remaining case, we prove NP-hardness. We also introduce and analyze a simpler type of door gadget, called the self-closing door. This gadget has two states and only two tunnels, similar to the "open" and "traverse" tunnels of doors, except that traversing the traverse tunnel also closes the door. In a variant called the symmetric self-closing door, the "open" tunnel can be traversed if and only if the door is closed. We prove that it is PSPACE-complete to decide whether an agent can move from one location to another through a planar assembly of either type of self-closing door. Then we apply this framework to prove new PSPACE-hardness results for eight different 3D Mario games and Sokobond.
Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping.

The mapping between the visual input on the retina to the cortical surface, i.e., retinotopic mapping, is an important topic in vision science and neuroscience. Human retinotopic mapping can be revealed by analyzing cortex functional magnetic resonance imaging (fMRI) signals when the subject is under specific visual stimuli. Conventional methods process, smooth, and analyze the retinotopic mapping based on the parametrization of the (partial) cortical surface. However, the retinotopic maps generated by this approach frequently contradict neuropsychology results. To address this problem, we propose an integrated approach that parameterizes the cortical surface, such that the parametric coordinates linearly relates the visual coordinate. The proposed method helps the smoothing of noisy retinotopic maps and obtains neurophysiological insights in human vision systems. One key element of the approach is the Error-Tolerant Teichmuller Map, which uniforms the angle distortion and maximizes the alignments to self-contradicting landmarks. We validated our overall approach with synthetic and real retinotopic mapping datasets. The experimental results show the proposed approach is superior in accuracy and compatibility. Although we focus on retinotopic mapping, the proposed framework is general and can be applied to process other human sensory maps.
Sapling: accelerating suffix array queries with learned data models

MOTIVATION As genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query, it requires widespread memory accesses, causing a large number of cache misses on large datasets. RESULTS Here, we present Sapling, an algorithm for sequence alignment, which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple widely used read aligners on a diverse collection of genomes, including human, bacteria and plants, speeding up the algorithm by more than a factor of two while adding <1% to the suffix array's memory footprint. AVAILABILITY AND IMPLEMENTATION The source code and tutorial are available open-source at https://github.com/mkirsche/sapling. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Democracy, America, and the age of globalization

Because political campaigns in the United States are privately funded, America's political system is heavily biased toward the interests of wealthy campaign contributors. As a result, government policies have largely ignored the growth in income inequality caused by technological change and economic globalization. This omission has been tolerated because most Americans do not support interventionist government policies. They believe that the government serves the interests of the campaign donors rather than the public. This skepticism concerning the public sector's fairness must be overcome before effective programs to offset mounting inequality can be implemented. Though in recent years legislation to reform the financing of political campaigns has been adopted, private wealth continues to dominate the political process. Political cynicism therefore persists. A voluntary system of public funding of candidates for office is required to generate the trust in the public sector necessary to reverse the trend toward inequality.
SET MAPPING REFLECTION

In this note we will discuss a new reflection principle which follows from the Proper Forcing Axiom. The immediate purpose will be to prove that the bounded form of the Proper Forcing Axiom implies both that 2ω = ω2 and that satisfies the Axiom of Choice. It will also be demonstrated that this reflection principle implies that □(κ) fails for all regular κ > ω1.
Property A and the existence of a Markov process with a trivial Poisson boundary

This note make the observation that property A for a space is equivalent to the existence of a Markov process on the space which has a (uniformly) trivial Poisson boundary.
Entanglement entropy with background gauge fields

We study the entanglement entropy, the Renyi entropy, and the mutual (Renyi) information of Dirac fermions on a 2 dimensional torus in the presence of constant gauge fields. We derive their general formulas using the equivalence between twisted boundary conditions and the background gauge fields. Novel and interesting physical consequences have been presented in arXiv:1705.01859 . Here we provide detailed computations of the entropies and mutual information in a low temperature limit, a large radius limit, and a high temperature limit. The high temperature limit reveals rather different physical properties compared to those of the low temperature one: there exist two non-trivial limits that depend on a modulus parameter and are not smoothly connected.
Large Deviations of the Free Energy in the O’Connell–Yor Polymer

We investigate large deviations of the free energy in the O’Connell–Yor polymer through a variational representation of the positive real moment Lyapunov exponents of the associated parabolic Anderson model. Our methods yield an exact formula for all real moment Lyapunov exponents of the parabolic Anderson model and a dual representation of the large deviation rate function with normalization \(n\) for the free energy.
SEMI-REGULAR MASAS OF TRANSFINITE LENGTH

In 1965, Tauer produced a countably infinite family of semi-regular masas in the hyperfinite II1 factor, no pair of which are conjugate by an automorphism. This was achieved by iterating the process of passing to the algebra generated by the normalizers and, for each n ∈ ℕ, finding masas for which this procedure terminates at the nth stage. Such masas are said to have length n. In this paper, we consider a transfinite version of this idea, giving rise to a notion of ordinal valued length. We show that all countable ordinals arise as lengths of semi-regular masas in the hyperfinite II1 factor. Furthermore, building on work of Jones and Popa, we obtain all possible combinations of regular inclusions of irreducible subfactors in the normalizing tower.
Graphene cantilever under Casimir force

The stability of graphene cantilever under Casimir attraction to an underlying conductor is investigated. The dependence of the instability threshold on temperature and flexural rigidity is obtained. Analytical work is supplemented by numerical computation of the critical temperature above which the graphene cantilever irreversibly bends down and attaches to the conductor. The geometry of the attachment and exfoliation of the graphene sheet is discussed. It is argued that graphene cantilever can be an excellent tool for precision measurements of the Casimir force.
Nonparametric Bayesian Instrumental Variable Analysis: Evaluating Heterogeneous Effects of Coronary Arterial Access Site Strategies.

Percutaneous coronary interventions (PCIs) are nonsurgical procedures to open blocked blood vessels to the heart, frequently using a catheter to place a stent. The catheter can be inserted into the...
Torsion-free Word Hyperbolic Groups are Noncommutatively Slender

In this paper, we prove the claim given in the title. A group G is noncommutatively slender if each map from the fundamental group of the Hawaiian Earring to G factors through projection to a canonical free subgroup. Higman, in his seminal 1952 paper [Unrestricted free products and varieties of topological groups, J. London Math. Soc. 27 (1952) 73–81], proved that free groups are noncommutatively slender. Such groups were first defined by Eda in [Free σ-products and noncommutatively slender groups, J. Algebra 148 (1992) 243–263]. Eda has asked which finitely presented groups are noncommutatively slender. This result demonstrates that random finitely presented groups in the few-relator sense are noncommutatively slender.
Sensitivity of the Geomagnetic Octupole to a Stably Stratified Layer in the Earth's Core

Current "Earth-like" numerical dynamo simulations are able to reproduce many characteristics of the observed geomagnetic field. One notable exception is the geomagnetic octupolar component. Here we investigate whether a stably stratified layer at the top of the core, a missing ingredient in standard dynamo simulations, can explain the observed geomagnetic octupole. Through numerical simulations, we find that the existence of a stable layer has significant influence on the octupolar-to-dipolar ratio of the magnetic field. Particularly, we find that a 60 km stable layer with relatively strong stability or a 130 km layer with relatively weak stability are compatible with the observations, but a 350 km stable layer, as suggested by recent seismological evidence, is not compatible with Earth's octupole field over the past 10,000 years.
Two Dimensional Array Imaging With Beam Steered Data

This paper discusses different approaches used for millimeter wave imaging of 2D objects. Imaging of a 2D object requires reflected wave data to be collected across two distinct dimensions. In this paper, we propose a reconstruction method that uses narrowband waveforms along with 2D beam steering. The beam is steered in azimuthal and elevation direction, which forms the two distinct dimensions required for the reconstruction. The reconstruction technique uses inverse Fourier transform along with amplitude and phase correction factors. In addition, this reconstruction technique does not require interpolation of the data in either wavenumber or spatial domain. Use of the 2D beam steering offers better performance in the presence of noise compared with the existing methods, such as switched array imaging system. Effects of radio-frequency impairments such as quantization of the phase of beam steering weights and timing jitter, which add to phase noise, are analyzed.
Finite-size corrections for ground states of Edwards-Anderson spin glasses

Extensive computations of ground-state energies of the Edwards-Anderson spin glass on bond-diluted, hypercubic lattices are conducted in dimensions d=3, ..., 7. Results are presented for bond densities exactly at the percolation threshold, p=pc, and deep within the glassy regime, p>pc, where finding ground states is one of the hardest combinatorial optimization problems. Finite-size corrections of the form 1/Nω are shown to be consistent throughout with the prediction ω=1−y/d, where y refers to the "stiffness" exponent that controls the formation of domain wall excitations at low temperatures. At p=pc, an extrapolation for d→∞ appears to match our mean-field results for these corrections. In the glassy phase, however, ω does not approach its anticipated mean-field value of 2/3, obtained from simulations of the Sherrington-Kirkpatrick spin glass on an N-clique graph. Instead, the value of ω reached at the upper critical dimension matches another type of mean-field spin glass models, namely those on sparse random networks of regular degree called Bethe lattices.
Naturally heavy superpartners and a Little Higgs

We construct an extension of the MSSM in which scalar superpartners can naturally be as heavy as 1 TeV. In the MSSM, the most significant fine tuning stems from the logarithmically enhanced top-stop loop contribution to the soft Higgs mass. We combine supersymmetry with the ``simplest little Higgs" to render this loop finite, thereby removing the large logarithm even in models in which superpartner masses are generated at high scales such as in supergravity. Our model predicts an extended Higgs sector, superpartner masses near a TeV and little Higgs partners at a few TeV.
Bulk viscosity of QCD matter near the critical temperature

Kubo's formula relates bulk viscosity to the retarded Green's function of the trace of the energy-momentum tensor. Using low energy theorems of QCD for the latter we derive the formula which relates the bulk viscosity to the energy density and pressure of hot matter. We then employ the available lattice QCD data to extract the bulk viscosity as a function of temperature. We find that close to the deconfinement temperature bulk viscosity becomes large, with viscosity-to-entropy ratio ζ/s ~ 1.
Entropy Anomaly in Langevin–Kramers Dynamics with a Temperature Gradient, Matrix Drag, and Magnetic Field

We investigate entropy production in the small-mass (or overdamped) limit of Langevin–Kramers dynamics. The results generalize previous works to provide a rigorous derivation that covers systems with magnetic field as well as anisotropic (i.e. matrix-valued) drag and diffusion coefficients that satisfy a fluctuation–dissipation relation with state-dependent temperature. In particular, we derive an explicit formula for the anomalous entropy production which can be estimated from simulated paths of the overdamped system. As a part of this work, we develop a theory for homogenizing a class of integral processes involving the position and scaled-velocity variables. This allows us to rigorously identify the limit of the entropy produced in the environment, including a bound on the convergence rate.
INVARIANCE OF CERTAIN PLURIGENERA FOR SURFACES IN MIXED CHARACTERISTICS

We prove the deformation invariance of Kodaira dimension and of certain plurigenera and the existence of canonical models for log surfaces which are smooth over an integral Noetherian scheme .
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion

We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation. We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 F1 points compared to the next-best method. Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.
Quasars Probing Quasars. II. The Anisotropic Clustering of Optically Thick Absorbers around Quasars

With close pairs of quasars at different redshifts, a background quasar sight line can be used to study a foreground quasar's environment in absorption. We used a sample of 17 Lyman limit systems with column density NH I > 1019 cm-2 selected from 149 projected quasar pair sight lines to investigate the clustering pattern of optically thick absorbers around luminous quasars at z ~ 2.5. Specifically, we measured the quasar-absorber correlation function in the transverse direction and found a comoving correlation length of r0 = 9.2 h-1 Mpc (comoving), assuming a power-law correlation function ξ ∝ r-γ, with γ = 1.6. Applying this transverse clustering strength to the line of sight, would predict that ~15%-50% of all quasars should show a NH I > 1019 cm-2 absorber within a velocity window of Δv < 3000 km s-1. This overpredicts the number of absorbers along the line of sight by a large factor, providing compelling evidence that the clustering pattern of optically thick absorbers around quasars is highly anisotropic. The most plausible explanation for the anisotropy is that the transverse direction is less likely to be illuminated by ionizing photons than the line of sight, and that absorbers along the line of sight are being photoevaporated. A simple model for the photoevaporation of absorbers subject to the ionizing flux of a quasar is presented, and it is shown that absorbers with volume densities nH 0.1 will be photoevaporated if they lie within ~1 Mpc (proper) of a luminous quasar. Using this simple model, we illustrate how comparisons of the transverse and line-of-sight clustering around quasars can ultimately be used to constrain the distribution of gas in optically thick absorption-line systems.
Controlling dispersive hydrodynamic wavebreaking in a viscous fluid conduit

Quantitative boundary control of buoyant, interfacial dynamics between two high-viscosity-contrast Stokes fluids is achieved by the technique of characteristic tracking. This enables the experimental generation of desired wave breaking profiles that produce dispersive shock waves and solitons.
Estimation of direct laser acceleration in laser wakefield accelerators using particle-in-cell simulations

Many current laser wakefield acceleration (LWFA) experiments are carried out in a regime where the laser pulse length is on the order of or longer than the wake wavelength and where ionization injection is employed to inject electrons into the wake. In these experiments, the electrons can gain a significant amount of energy from the direct laser acceleration (DLA) mechanism as well as the usual LWFA mechanism. Particle-in-cell (PIC) codes are frequently used to discern the relative contribution of these two mechanisms. However, if the longitudinal resolution used in the PIC simulations is inadequate, it can produce numerical heating that can overestimate the transverse motion, which is important in determining the energy gain due to DLA. We have therefore carried out a systematic study of this LWFA regime by varying the longitudinal resolution of PIC simulations and then examining the energy gain characteristics of both the highest-energy electrons and the bulk electrons. By calculating the contribution of DLA to the final energies of the electrons produced from the LWFA, we find that even at the highest longitudinal resolutions, DLA contributes a significant portion of the energy gained by the highest-energy electrons and also contributes to accelerating the bulk of the charge in the electron beam produced by the LWFA.
Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from high-dimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this letter has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that, we introduce a nonparametric Bayesian learning method—a sticky hierarchical Dirichlet process hidden Markov model—to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.
The variable quality of metadata about biological samples used in biomedical experiments.

We present an analytical study of the quality of metadata about samples used in biomedical experiments. The metadata under analysis are stored in two well-known databases: BioSample—a repository managed by the National Center for Biotechnology Information (NCBI), and BioSamples—a repository managed by the European Bioinformatics Institute (EBI). We tested whether 11.4 M sample metadata records in the two repositories are populated with values that fulfill the stated requirements for such values. Our study revealed multiple anomalies in the metadata. Most metadata field names and their values are not standardized or controlled. Even simple binary or numeric fields are often populated with inadequate values of different data types. By clustering metadata field names, we discovered there are often many distinct ways to represent the same aspect of a sample. Overall, the metadata we analyzed reveal that there is a lack of principled mechanisms to enforce and validate metadata requirements. The significant aberrancies that we found in the metadata are likely to impede search and secondary use of the associated datasets.
The habitable-zone planet finder: A stabilized fiber-fed NIR spectrograph for the Hobby-Eberly telescope

We present the scientic motivation and conceptual design for the recently funded Habitable-zone Planet Finder (HPF), a stabilized ber-fed near-infrared (NIR) spectrograph for the 10 meter class Hobby-Eberly Telescope (HET) that will be capable of discovering low mass planets around M dwarfs. The HPF will cover the NIR Y & J bands to enable precise radial velocities to be obtained on mid M dwarfs, and enable the detection of low mass planets around these stars. The conceptual design is comprised of a cryostat cooled to 200K, a dual ber-feed with a science and calibration ber, a gold coated mosaic echelle grating, and a Teledyne Hawaii-2RG (H2RG) NIR detector with a 1.7 m cuto. A uranium-neon hollow-cathode lamp is the baseline wavelength calibration source, and we are actively testing laser frequency combs to enable even higher radial velocity precision. We will present the overall instrument system design and integration with the HET, and discuss major system challenges, key choices, and ongoing research and development projects to mitigate risk. We also discuss the ongoing process of target selection for the HPF survey.