cssi/SciSciGPT-SciSciNet huggingface dataset:
The Perception and Excessive Valuation of Small, Publicized Drinking Water Risks

Low probability risks create challenges for individual decisions and potential pressures for government regulation. This article reports original survey evidence regarding the public’s perception and valuation of water-related risks from plastic bottles with bisphenol A, residues in drinking water of the herbicide atrazine, and trace amounts of prescription drugs in water. People who believe that they face high water-related risks generally believe that the risks apply and, given that belief, are willing to pay more to limit the risk. However, the expressed willingness to pay for risk reductions is inordinately high even among those who are unsure of whether they are even exposed to the risk, and therefore may not be reliable as values for the actual benefits.
Binary Sequence Set Design for Interferer Rejection in Multi-Branch Modulation

Wideband communication is often expected to deal with a very wide spectrum, thus receivers for the wideband communication systems often need to mitigate interferers to reduce the distortion caused by the amplifier nonlinearity and noise. Recently, the random modulation mixes a signal with different pseudorandom sequences using multiple branches of channels before sampling. In previous work, we introduced the design of a single spectrally shaped binary sequence that mitigates interferers to replace the pseudorandom sequence in a channel. However, the designed sequences cannot provide a stable recovery achieved by pseudorandom sequence approaches. In this paper, we extend our previous sequence design to guarantee stable recovery by designing a set of spectrally shaped sequences to be orthogonal to each other. We show that it is difficult to find the necessary number of sequences (equal to the sequence length) featuring mutual orthogonality and introduce oversampling to the sequence set design to improve the recovery performance of the designed sequences. We propose an algorithm for multibranch sequence design as a binary optimization problem, which is solved using a semidefinite program relaxation and randomized projection. In addition, while it is common to model narrowband interferers as a subspace spanned by a subset of elements from the Fourier basis, we show that the Slepian basis provides an alternative and more suitable compact representation for signals with components contained in narrow spectrum bands. Numerical experiments using the proposed sequence sets show their advantages against pseudorandom sequences and our previous work.
Dual Policy Distillation

Policy distillation, which transfers a teacher policy to a student policy has achieved great success in challenging tasks of deep reinforcement learning. This teacher-student framework requires a well-trained teacher model which is computationally expensive. Moreover, the performance of the student model could be limited by the teacher model if the teacher model is not optimal. In the light of collaborative learning, we study the feasibility of involving joint intellectual efforts from diverse perspectives of student models. In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning. The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms, since it is unclear whether the knowledge distilled from an imperfect and noisy peer learner would be helpful. To address the challenge, we theoretically justify that distilling knowledge from a peer learner will lead to policy improvement and propose a disadvantageous distillation strategy based on the theoretical results. The conducted experiments on several continuous control tasks show that the proposed framework achieves superior performance with a learning-based agent and function approximation without the use of expensive teacher models.
Evolutionarily conserved transcription factors drive the oxidative stress response in Drosophila

As organisms are constantly exposed to the damaging effects of oxidative stress through both environmental exposure and internal metabolic processes, they have evolved a variety of mechanisms to cope with this stress. One such mechanism is the highly conserved p38 MAPK (p38K) pathway, which is known to be post-translationally activated in response to oxidative stress, resulting in the activation of downstream antioxidant targets. However, little is known about the role of p38K transcriptional regulation in response to oxidative stress. Therefore, we analyzed the p38K gene family across the genus Drosophila to identify conserved regulatory elements. We found that oxidative stress exposure results in increased p38K protein levels in multiple Drosophila species and is associated with increased oxidative stress resistance. We also found that the p38Kb genomic locus includes conserved AP-1 and lola-PT transcription factor consensus binding sites. Accordingly, over-expression of these transcription factors in D. melanogaster is sufficient to induce transcription of p38Kb and enhances resistance to oxidative stress. We further found that the presence of a putative lola-PT binding site in the p38Kb locus of a given species is predictive of the species' survival in response to oxidative stress. Through our comparative genomics approach, we have identified biologically relevant putative transcription factor binding sites that regulate the expression of p38Kb and are associated with resistance to oxidative stress. These findings reveal a novel mode of regulation for p38K genes and suggest that transcription may play as important a role in p38K-mediated stress responses as post-translational modifications.
Experimental Effects of “Achievement Gap” News Reporting on Viewers’ Racial Stereotypes, Inequality Explanations, and Inequality Prioritization:

The “achievement gap” has long dominated mainstream conversations about race and education. Some scholars warn that the discourse around racial gaps perpetuates stereotypes and promotes the adoptio...
Information-Theoretic Local Minima Characterization and Regularization

Recent advances in deep learning theory have evoked the study of generalizability across different local minima of deep neural networks (DNNs). While current work focused on either discovering properties of good local minima or developing regularization techniques to induce good local minima, no approach exists that can tackle both problems. We achieve these two goals successfully in a unified manner. Specifically, based on the observed Fisher information we propose a metric both strongly indicative of generalizability of local minima and effectively applied as a practical regularizer. We provide theoretical analysis including a generalization bound and empirically demonstrate the success of our approach in both capturing and improving the generalizability of DNNs. Experiments are performed on CIFAR-10, CIFAR-100 and ImageNet for various network architectures.
Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys

The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a “configurationally-resolved planar fault” (CRPF) energy and extend the cluster expansion method from alloy theory to express the CRPF as a function of chemical occupation variables of sites surrounding the fault. We apply the approach to explore the composition and temperature dependence of the unstable stacking fault energy (USF) in binary Mo–Nb alloys. First-principles calculations are used to parameterize a formation energy and CRPF cluster expansion. Monte Carlo simulations show that the distribution of USF energies is significantly affected by chemical composition and temperature. The formalism is broadly applicable to arbitrary crystal structures and alloy chemistries and will enable the development of rigorous models for deformation mechanisms in high-entropy alloys.
Genericity and Universality for Operator Ideals

A bounded linear operator $U$ between Banach spaces is universal for the complement of some operator ideal $\mathfrak{J}$ if it is a member of the complement and it factors through every element of the complement of $\mathfrak{J}$. In the first part of this paper, we produce new universal operators for the complements of several ideals and give examples of ideals whose complements do not admit such operators. In the second part of the paper, we use Descriptive Set Theory to study operator ideals. After restricting attention to operators between separable Banach spaces, we call an operator ideal $\mathfrak{J}$ generic if anytime an operator $A$ has the property that every operator in $\mathfrak{J}$ factors through a restriction of $A$, then every operator between separable Banach spaces factors through a restriction of $A$. We prove that many of classical operator ideals are generic (i.e. strictly singular, weakly compact, Banach-Saks) and give a sufficient condition, based on the complexity of the ideal, for when the complement does not admit a universal operator. Another result is a new proof of a theorem of M. Girardi and W.B. Johnson which states that there is no universal operator for the complement of the ideal of completely continuous operators.
GapMind: Automated Annotation of Amino Acid Biosynthesis.

GapMind is a Web-based tool for annotating amino acid biosynthesis in bacteria and archaea (http://papers.genomics.lbl.gov/gaps). GapMind incorporates many variant pathways and 130 different reactions, and it analyzes a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins. GapMind correctly handles fusion proteins and split proteins, which often cause errors for best-hit approaches. To improve GapMind's coverage, we examined genetic data from 35 bacteria that grow in defined media without amino acids, and we filled many gaps in amino acid biosynthesis pathways. For example, we identified additional genes for arginine synthesis with succinylated intermediates in Bacteroides thetaiotaomicron, and we propose that Dyella japonica synthesizes tyrosine from phenylalanine. Nevertheless, for many bacteria and archaea that grow in minimal media, genes for some steps still cannot be identified. To help interpret potential gaps, GapMind checks if they match known gaps in related microbes that can grow in minimal media. GapMind should aid the identification of microbial growth requirements.IMPORTANCE Many microbes can make all of the amino acids (the building blocks of proteins). In principle, we should be able to predict which amino acids a microbe can make, and which it requires as nutrients, by checking its genome sequence for all of the necessary genes. However, in practice, it is difficult to check for all of the alternative pathways. Furthermore, new pathways and enzymes are still being discovered. We built an automated tool, GapMind, to annotate amino acid biosynthesis in bacterial and archaeal genomes. We used GapMind to list gaps: cases where a microbe makes an amino acid but a complete pathway cannot be identified in its genome. We used these gaps, together with data from mutants, to identify new pathways and enzymes. However, for most bacteria and archaea, we still do not know how they can make all of the amino acids.
Optimal subsampling for quantile regression in big data

We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former does not depend on the densities of the responses given covariates and is easy to implement. Algorithms based on optimal subsampling probabilities are proposed and asymptotic distributions and asymptotic optimality of the resulting estimators are established. Furthermore, we propose an iterative subsampling procedure based on the optimal subsampling probabilities in the linearly transformed parameter estimation which has great scalability to utilize available computational resources. In addition, this procedure yields standard errors for parameter estimators without estimating the densities of the responses given the covariates. We provide numerical examples based on both simulated and real data to illustrate the proposed method.
FERMAT'S LAST THEOREM ALGEBRAIC PROOF

In 1995, A, Wiles announced, using cyclic groups ( a subject area which was not available at the time of Fermat), a proof of Fermat's Last Theorem, which is stated as follows: If is an odd prime and x; y; z; are relatively prime positive integers, then z 6= x + y: In this note, an elegant proof of this result is given. It is proved, using elementary algebra, that if is an odd prime and x; y; z are positive integers satisfying z = x+y; then z; x; y are each divisible by :
Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies

In this letter we present a multi-rate control architecture for safety critical systems. We consider a high level planner and a low level controller which operate at different frequencies. This multi-rate behavior is described by a piecewise nonlinear model which evolves on a continuous and a discrete level. First, we present sufficient conditions which guarantee recursive constraint satisfaction for the closed-loop system. Afterwards, we propose a control design methodology which leverages Control Barrier Functions (CBFs) for low level control and Model Predictive Control (MPC) policies for high level planning. The control barrier function is designed using the full nonlinear dynamical model and the MPC is based on a simplified planning model. When the nonlinear system is control affine and the high level planning model is linear, the control actions are computed by solving convex optimization problems at each level of the hierarchy. Finally, we show the effectiveness of the proposed strategy on a simulation example, where the low level control action is updated at a higher frequency than the high level command.
Precoder Design for mmWave UAV Communications with Physical Layer Security

The integration of unmanned aerial vehicles (UAVs) into the terrestrial cellular networks is envisioned as one key technology for next-generation wireless communications. In this work, we consider the physical layer security of the communications links in the millimeter-wave (mmWave) spectrum which are maintained by UAVs functioning as base stations (BS). In particular, we propose a new precoding strategy which incorporates the channel state information (CSI) of the eavesdropper (Eve) compromising link security. We show that our proposed precoder strategy eliminates any need for artificial noise (AN) transmission in underloaded scenarios (fewer users than number of antennas). In addition, we demonstrate that our nonlinear precoding scheme provides promising secrecy-rate performance even for overloaded scenarios at the expense of transmitting low-power AN.
A numerical toolkit for multiprojective varieties

A numerical description of an algebraic subvariety of projective space is given by a general linear section, called a witness set. For a subvariety of a product of projective spaces (a multiprojective variety), the corresponding numerical description is given by a witness collection, whose structure is more involved. We build on recent work to develop a toolkit for the numerical manipulation of multiprojective varieties that operates on witness collections, and use this toolkit in an algorithm for numerical irreducible decomposition of multiprojective varieties. The toolkit and decomposition algorithm are illustrated throughout in a series of examples.
DNA-PKcs phosphorylation at the T2609 cluster alters the repair pathway choice during immunoglobulin class switch recombination

The DNA-dependent protein kinase (DNA-PK), which is composed of the KU heterodimer and the large catalytic subunit (DNA-PKcs), is a classical nonhomologous end-joining (cNHEJ) factor. Naive B cells undergo class switch recombination (CSR) to generate antibodies with different isotypes by joining two DNA double-strand breaks at different switching regions via the cNHEJ pathway. DNA-PK and the cNHEJ pathway play important roles in the DNA repair phase of CSR. To initiate cNHEJ, KU binds to DNA ends and recruits and activates DNA-PK. Activated DNA-PK phosphorylates DNA-PKcs at the S2056 and T2609 clusters. Loss of T2609 cluster phosphorylation increases radiation sensitivity but whether T2609 phosphorylation has a role in physiological DNA repair remains elusive. Using the DNA-PKcs5A mouse model carrying alanine substitutions at the T2609 cluster, here we show that loss of T2609 phosphorylation of DNA-PKcs does not affect the CSR efficiency. Yet, the CSR junctions recovered from DNA-PKcs5A/5A B cells reveal increased chromosomal translocations, extensive use of distal switch regions (consistent with end resection), and preferential usage of microhomology—all signs of the alternative end-joining pathway. Thus, these results uncover a role of DNA-PKcs T2609 phosphorylation in promoting cNHEJ repair pathway choice during CSR.
Model to Hardware matching for nano-meter scale technologies

With technology scaling becoming ever more difficult, the drive to continue to deliver performance and density has led to increasing technology complexity. Examples include the pervasive application of resolution enhancement techniques (RET) to enable sub-wavelength lithography and achieve circuit density, and strain engineering to improve device mobility and achieve circuit performance. The result of this increasing technology complexity has been a corresponding increase in the complexity of design/technology interaction. This phenomena demonstrates itself as a drastic increase in the number and complexity of design rules. Many of these rules are the result of the increase of the number and magnitude of systematic effects. In addition to these systematic sources of variability, we have an increasing host of random variations such as line edge roughness, which impacts channel lengths, and random dopant fluctuations, which impact threshold voltage. The net result has been a reduction in our ability to reliably predict the outcome of the manufacturing process. Given that the integrated circuit design process is based completely on our ability to create computer models of the expected behavior of a design, this gap in predictability is a source of grave concern. Model to hardware matching attempts to close this gap by developing techniques, tools, and design components which can be used to improve technology predictability.
The Implementation Research Logic Model: a method for planning, executing, reporting, and synthesizing implementation projects.

Numerous models, frameworks, and theories exist for specific aspects of implementation research, including for determinants, strategies, and outcomes. However, implementation research projects often fail to provide a coherent rationale or justification for how these aspects are selected and tested in relation to one another. Despite this need to better specify the conceptual linkages between the core elements involved in projects, few tools or methods have been developed to aid in this task. The Implementation Research Logic Model (IRLM) was created for this purpose and to enhance the rigor and transparency of describing the often-complex processes of improving the adoption of evidence-based interventions in healthcare delivery systems. The IRLM structure and guiding principles were developed through a series of preliminary activities with multiple investigators representing diverse implementation research projects in terms of contexts, research designs, and implementation strategies being evaluated. The utility of the IRLM was evaluated in the course of a 2-day training to over 130 implementation researchers and healthcare delivery system partners. Preliminary work with the IRLM produced a core structure and multiple variations for common implementation research designs and situations, as well as guiding principles and suggestions for use. Results of the survey indicated a high utility of the IRLM for multiple purposes, such as improving rigor and reproducibility of projects; serving as a “roadmap” for how the project is to be carried out; clearly reporting and specifying how the project is to be conducted; and understanding the connections between determinants, strategies, mechanisms, and outcomes for their project. The IRLM is a semi-structured, principle-guided tool designed to improve the specification, rigor, reproducibility, and testable causal pathways involved in implementation research projects. The IRLM can also aid implementation researchers and implementation partners in the planning and execution of practice change initiatives. Adaptation and refinement of the IRLM are ongoing, as is the development of resources for use and applications to diverse projects, to address the challenges of this complex scientific field.
Bicycle attacks considered harmful: Quantifying the damage of widespread password length leakage

Abstract This work examines the issue of password length leakage via encrypted traffic i.e., bicycle attacks. We aim to quantify both the prevalence of password length leakage bugs as well as the potential harm to users. We discuss several ways in which an eavesdropping attacker could link this password length with a particular user account e.g., a targeted campaign against a smaller group of users or via DNS hijacking for larger scale campaigns. We next use a decision-theoretic model to quantify the extent to which password length leakage might help an attacker to crack user passwords. In our analysis, we consider three different levels of password attackers: hacker, criminal and nation-state. In all cases, we find that such an attacker who knows the length of each user password gains a significant advantage over one without knowing the password length. As part of this analysis, we also release a new differentially private password frequency dataset from the 2016 LinkedIn breach using a differentially private algorithm of Blocki et al. (NDSS 2016) to protect user accounts. We advocate for a new W3C standard on how password fields are handled which would effectively eliminate most instances of password length leakage.
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph

3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for optimal performance. Extensive experiments show that our method can successfully estimate the 3D structure of the cavity and reflect the details for each tooth. Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly outperforms the encoder-decoder method by \(1.71{\times }\) and the retrieval-based method by \(1.52{\times }\). Our method can also be promising for other multi-anatomy 3D reconstruction tasks.
Social Distancing and the Economic Impact of COVID-19 in the United States

This study documents how the demographics of new infections and mortality changed over time across US counties. We ﬁnd that counties with a larger population share aged above 60 were hit harder initially in terms of both cases and mortality in March and April while counties with a larger population share aged below 20 were hit harder in June and July. At the same time, how counties that voted Democratic in 2016 are aﬀected does not change over time. Subsequently, we simulate an alternative evolution of the pandemic, assuming that states extended the lockdown measures until daily new cases reach the levels of European countries after their lockdown measures were relaxed. In the baseline simulation, we ﬁnd that cases and deaths would have increased by around 50% less by the end of June, but it would have led to a 2 percentage point larger drop in Q2 GDP.
Aristotle on the Nature and Politics of Medicine

According to Aristotle, the medical art aims at health, which is a virtue of the body, and does so in an unlimited way. Consequently, medicine does not determine the extent to which health should be pursued, and “mental health” falls under medicine only via pros hen predication. Because medicine is inherently oriented to its end, it produces health in accordance with its nature and disease contrary to its nature — even when disease is good for the patient. Aristotle’s politician understands that this inherent orientation can be systematically distorted, and so would see the need for something like the Hippocratic Oath.
Hope Speech Detection: A Computational Analysis of the Voice of Peace.

The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an analysis on temporal trends of pro-peace and pro-war intent observing that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}.
The generalized trust region subproblem: solution complexity and convex hull results

We consider the generalized trust region subproblem (GTRS) of minimizing a nonconvex quadratic objective over a nonconvex quadratic constraint. A lifting of this problem recasts the GTRS as minimizing a linear objective subject to two nonconvex quadratic constraints. Our first main contribution is structural: we give an explicit description of the convex hull of this nonconvex set in terms of the generalized eigenvalues of an associated matrix pencil. This result may be of interest in building relaxations for nonconvex quadratic programs. Moreover, this result allows us to reformulate the GTRS as the minimization of two convex quadratic functions in the original space. Our next set of contributions is algorithmic: we present an algorithm for solving the GTRS up to an $$\epsilon $$ additive error based on this reformulation. We carefully handle numerical issues that arise from inexact generalized eigenvalue and eigenvector computations and establish explicit running time guarantees for these algorithms. Notably, our algorithms run in linear (in the size of the input) time. Furthermore, our algorithm for computing an $$\epsilon $$ -optimal solution has a slightly-improved running time dependence on $$\epsilon $$ over the state-of-the-art algorithm. Our analysis shows that the dominant cost in solving the GTRS lies in solving a generalized eigenvalue problem—establishing a natural connection between these problems. Finally, generalizations of our convex hull results allow us to apply our algorithms and their theoretical guarantees directly to equality-, interval-, and hollow-constrained variants of the GTRS. This gives the first linear-time algorithm in the literature for these variants of the GTRS.
Logical Clifford Synthesis for Stabilizer Codes

Quantum error-correcting codes are used to protect qubits involved in quantum computation. This process requires logical operators to be translated into physical operators acting on physical quantum states. In this article, we propose a mathematical framework for synthesizing physical circuits that implement logical Clifford operators for stabilizer codes. Circuit synthesis is enabled by representing the desired physical Clifford operator in $\mathbb {C}^{N \times N}$ as a $2m \times 2m$ binary symplectic matrix, where $N = 2^m$ . We prove two theorems that use symplectic transvections to efficiently enumerate all binary symplectic matrices that satisfy a system of linear equations. As a corollary, we prove that for an $[\![m,k ]\!]$ stabilizer code every logical Clifford operator has $2^{r(r+1)/2}$ symplectic solutions, where $r = m-k$ , up to stabilizer degeneracy. The desired physical circuits are then obtained by decomposing each solution into a product of elementary symplectic matrices, that correspond to elementary circuits. This enumeration of all physical realizations enables optimization over the ensemble with respect to a suitable metric. Furthermore, we show that any circuit that normalizes the stabilizer can be transformed into a circuit that centralizes the stabilizer, while realizing the same logical operation. Our method of circuit synthesis can be applied to any stabilizer code, and this paper discusses a proof of concept synthesis for the $[\![6,4,2 ]\!]$ CSS code. Programs implementing the algorithms in this article, which includes routines to solve for binary symplectic solutions of general linear systems and our overall LCS (logical circuit synthesis) algorithm, can be found at https://github.com/nrenga/symplectic-arxiv18a
Time-Resolved, Single-Molecule, Correlated Chemical Probing of RNA

Capturing the folding dynamics of large, functionally important RNAs has relied primarily on global measurements of structure or on per-nucleotide chemical probing. These approaches infer, but do not directly measure, through-space structural interactions. Here we introduce trimethyloxonium (TMO) as a chemical probe for RNA. TMO alkylates RNA at high levels in seconds, and thereby enables time-resolved, single-molecule, through-space probing of RNA folding using the RING-MaP correlated chemical probing framework. Time-resolved correlations in the RNase P RNA-a functional RNA with a complex structure stabilized by multiple noncanonical interactions-revealed that a long-range tertiary interaction guides native RNA folding for both secondary and tertiary structure. This unanticipated nonhierarchical folding mechanism was directly validated by examining the consequences of concise disruption of the through-space interaction. Single-molecule, time-resolved RNA structure probing with TMO is poised to reveal a wide range of dynamic RNA folding processes and principles of RNA folding.
The Influence of State-Level Production Outcomes Upon U.S. National Corn and Soybean Production: A Novel Application of Correlated Component Regression

The relative importance of key state-level outcomes upon U.S. national corn and soybean production was examined using correlated component regression, a recently developed regression technique for application to multicollinear and sparse data sets. Standardized coefficients were used to rank the states’ relative importance. A Herfindahl-Hirschman Index was used to measure the degree of concentration among the top ranked states. The empirical analysis looked at two time periods: a pre-Genetic Modification (1975–1995) and a post-Genetic Modification (1996–2017) period. The results indicate that U.S. corn production is becoming less geographically concentrated in terms of state-level importance while the opposite holds true for soybean production.
Time cells in the human hippocampus and entorhinal cortex support episodic memory

The organization of temporal information is critical for the encoding and retrieval of episodic memories. In the rodent hippocampus and entorhinal cortex, evidence accumulated over the last decade suggests that populations of "time cells" in the hippocampus encode temporal information. We identify time cells in humans using intracranial microelectrode recordings obtained from 27 human epilepsy patients who performed an episodic memory task. We show that time cell activity predicts the temporal organization of retrieved memory items. We also uncover evidence of ramping cell activity in humans, which represents a complementary type of temporal information. These findings establish a cellular mechanism for the representation of temporal information in the human brain needed to form episodic memories.
The Scientific and Historical Value of Annotations on Astronomical Photographic Plates

The application of photography to astronomy was a critical step in the development of astrophysics at the end of the nineteenth century. Using custom-built photographic telescopes and objective prisms, astronomers took images of the sky on glass plates during a 100-year period from many observing stations around the globe. After each plate was developed, astronomers and their assistants studied and annotated the plates as they made astrometric, photometric, and spectroscopic measurements, counted galaxies, observed stellar variability, tracked meteors, and calculated the ephemerides of asteroids and comets. In this paper, the authors assess the importance of the plate annotations for future scientific, historical, and educational programmes. Unfortunately, many of these interesting annotations are now being erased when grime is removed from the plates before they are digitized to make the photometric data available for time-domain astrophysics. To see what professional astronomers and historians think abo...
Enriched Lawvere Theories for Operational Semantics

Author(s): Baez, John C; Williams, Christian | Abstract: Enriched Lawvere theories are a generalization of Lawvere theories that allow us to describe the operational semantics of formal systems. For example, a graph enriched Lawvere theory describes structures that have a graph of operations of each arity, where the vertices are operations and the edges are rewrites between operations. Enriched theories can be used to equip systems with operational semantics, and maps between enriching categories can serve to translate between different forms of operational and denotational semantics. The Grothendieck construction lets us study all models of all enriched theories in all contexts in a single category. We illustrate these ideas with the SKI-combinator calculus, a variable-free version of the lambda calculus.
The dynamical diquark model: fine structure and isospin

We incorporate fine-structure corrections into the dynamical diquark model of multiquark exotic hadrons. These improvements include effects due to finite diquark size, spin-spin couplings within the diquarks, and most significantly, isospin-dependent couplings in the form of pionlike exchanges assumed to occur between the light quarks within the diquarks. Using a simplified two-parameter interaction Hamiltonian, we obtain fits in which the isoscalar JPC = 1++ state — identified as the X (3872) — appears naturally as the lightest exotic (including all states that are predicted by the model but have not yet been observed), while the closed-charm decays of Zc(3900) and Zc(4020) prefer J/𝜓 and hc modes, respectively, in accord with experiment. We explore implications of this model for the excited tetraquark multiplets and the pentaquarks.
Modeling nonlocal electron–phonon coupling in organic crystals using interpolative maps: The spectroscopy of crystalline pentacene and 7,8,15,16-tetraazaterrylene

Electron–phonon coupling plays a central role in the transport properties and photophysics of organic crystals. Successful models describing charge- and energy-transport in these systems routinely include these effects. Most models for describing photophysics, on the other hand, only incorporate local electron–phonon coupling to intramolecular vibrational modes, while nonlocal electron–phonon coupling is neglected. One might expect nonlocal coupling to have an important effect on the photophysics of organic crystals because it gives rise to large fluctuation in the charge-transfer couplings, and charge-transfer couplings play an important role in the spectroscopy of many organic crystals. Here, we study the effects of nonlocal coupling on the absorption spectrum of crystalline pentacene and 7,8,15,16-tetraazaterrylene. To this end, we develop a new mixed quantum–classical approach for including nonlocal coupling into spectroscopic and transport models for organic crystals. Importantly, our approach does not assume that the nonlocal coupling is linear, in contrast to most modern charge-transport models. We find that the nonlocal coupling broadens the absorption spectrum non-uniformly across the absorption line shape. In pentacene, for example, our model predicts that the lower Davydov component broadens considerably more than the upper Davydov component, explaining the origin of this experimental observation for the first time. By studying a simple dimer model, we are able to attribute this selective broadening to correlations between the fluctuations of the charge-transfer couplings. Overall, our method incorporates nonlocal electron–phonon coupling into spectroscopic and transport models with computational efficiency, generalizability to a wide range of organic crystals, and without any assumption of linearity.
Variability in the Atmosphere of the Hot Jupiter Kepler-76b

Phase curves and secondary eclipses of gaseous exoplanets are diagnostic of atmospheric composition and meteorology, and the long observational baseline and high photometric precision from the Kepler Mission make its dataset well-suited for exploring phase curve variability, which provides additional insights into atmospheric dynamics. Observations of the hot Jupiter Kepler-76b span more than 1,000 days, providing an ideal dataset to search for atmospheric variability. In this study, we find that Kepler-76b's secondary eclipse, with a depth of $87 \pm 6$ parts-per-million (ppm), corresponds to an effective temperature of 2,830$^{+50}_{-30}$ K. Our results also show clear indications of variability in Kepler-76b's atmospheric emission and reflectivity, with the phase curve amplitude typically $50.5 \pm 1.3$ ppm but varying between 35 and 70 ppm over tens of days. As is common for hot Jupiters, Kepler-76b's phase curve shows a discernible offset of $\left( 9 \pm 1.3 \right)^\circ$ eastward of the sub-stellar point and varying in concert with the amplitude. These variations may arise from the advance and retreat of thermal structures and cloud formations in Kepler-76b's atmosphere; the resulting thermal perturbations may couple with the super-rotation expected to transport aerosols, giving rise to a feedback loop. Looking forward, the TESS Mission can provide new insight into planetary atmospheres, with good prospects to observe both secondary eclipses and phase curves among targets from the mission. TESS's increased sensitivity in red wavelengths as compared to Kepler means that it will probably probe different aspects of planetary atmospheres.
Toward a Nonlocal Theory of Gravitation

The nonlocal theory of accelerated systems is extended to linear gravitational waves as measured by accelerated observers in Minkowski spacetime. The implications of this approach are discussed. In particular, the nonlocal modifications of helicity-rotation coupling are pointed out and a nonlocal wave equation is presented for a special class of uniformly rotating observers. The results of this study, via Einstein's heuristic principle of equivalence, provide the incentive for a nonlocal classical theory of the gravitational field.
Simultaneous Localization and Layout Model Selection in Manhattan Worlds

In this letter, we will demonstrate how Manhattan structure can be exploited to transform the simultaneous localization and mapping problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher-order layout structures, namely walls, floors, and ceilings. Furthermore, we show how our novel formulation leads to an optimization procedure that automatically performs data association and loop closure and which ultimately produces the simplest model of the environment that is consistent with the available measurements. We verify our method on real-world data sets collected with various sensing modalities.
Conformal interfaces between free boson orbifold theories

We construct a large class of conformal interfaces between two-dimensional c = 1 conformal field theories describing compact free bosons and their $$ {\mathrm{\mathbb{Z}}}_2 $$ orbifolds. The interfaces are obtained by constructing boundary states in the corresponding c = 2 product theories and applying the unfolding procedure. We compute the fusion products for all of these defects, and identify the invertible topological interfaces associated to global symmetries, the interfaces corresponding to marginal deformations, and the interfaces which map the untwisted sector of an orbifold to the invariant states of the parent theory.
Discovery of a Highly Energetic X-Ray Pulsar Powering HESS J1813-178 in the Young Supernova Remnant G12.82-0.02

We report the discovery of 44.7 ms pulsations from the X-ray source CXOU J181335.1–174957 using data obtained with the XMM-Newton Observatory. PSR J1813–1749 lies near the center of the young radio supernova remnant G12.82–0.02, which overlaps the compact TeV source HESS J1813–178. This rotation-powered pulsar is the second most energetic in the Galaxy, with a spin-down luminosity of erg s–1. In the rotating dipole model, the surface dipole magnetic field strength is Bs = (2.7 ± 0.6) × 1012 G and the spin-down age -7.5 kyr, consistent with the location in the small, shell-type radio remnant. At an assumed distance of 4.7 kpc by association with an adjacent young stellar cluster, the efficiency of PSR J1813–1749 in converting spin-down luminosity to radiation is 0.03% for its 2-10 keV flux, 0.1% for its 20-100 keV INTEGRAL flux, and 0.07% for the >200 GeV emission of HESS J1813–178, making it a likely power source for the latter. The nearby young stellar cluster is possibly the birthplace of the pulsar progenitor, as well as an additional source of seed photons for inverse Compton scattering to TeV energies.
Pessimal packing shapes

We address the question of which convex shapes, when packed as densely as possible under certain restrictions, fill the least space and leave the most empty space. In each different dimension and under each different set of restrictions, this question is expected to have a different answer or perhaps no answer at all. As the problem of identifying global minima in most cases appears to be beyond current reach, in this paper we focus on local minima. We review some known results and prove these new results: in two dimensions, the regular heptagon is a local minimum of the double-lattice packing density, and in three dimensions, the directional derivative (in the sense of Minkowski addition) of the double-lattice packing density at the point in the space of shapes corresponding to the ball is in every direction positive.
Streaming graph challenge: Stochastic block partition

An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.
Electroforming-free TaOx memristors using focused ion beam irradiations

We demonstrate creation of electroforming-free TaOx memristive devices using focused ion beam irradiations to locally define conductive filaments in TaOx films. Electrical characterization shows that these irradiations directly create fully functional memristors without the need for electroforming. Ion beam forming of conductive filaments combined with state-of-the-art nano-patterning presents a CMOS compatible approach to wafer-level fabrication of fully formed operational memristors.
New Constraints on Early-type Galaxy Assembly from Spectroscopic Metallicities of Globular Clusters in M87

The observed characteristics of globular cluster (GC) systems, such as metallicity distributions, are commonly used to place constraints on galaxy formation models. However, obtaining reliable metallicity values is particularly difficult because of our limited means to obtain high quality spectroscopy of extragalactic GCs. Often, `color--metallicity relations' are invoked to convert easier-to-obtain photometric measurements into metallicities, but there is no consensus on what form these relations should take. In this paper we make use of multiple photometric datasets and iron metallicity values derived from applying full-spectrum stellar population synthesis models to deep Keck/LRIS spectra of 177 GCs centrally located around M87 to obtain a new color--metallicity relation. Our new relation differs substantially from previous relations in the blue, and we present evidence that the M87 relation differs from that of the Milky Way GCs, suggesting environmental dependence of GC properties. We use our color--metallicity relation to derive a new GC metallicity-host galaxy luminosity relation for red and blue GCs and find a shallower relation for the blue GCs than what previous work has found and that the metal-poor GCs are more enriched than what was previously found. This could indicate that the progenitor satellite galaxies that now make up the stellar halos of early-type galaxies are more massive and formed later than previously thought, or that the properties of metal-poor GCs are less dependent on their present-day host, indicating a common origin.