cssi/SciSciGPT-SciSciNet huggingface dataset:
No-Regret and Incentive-Compatible Online Learning

We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm’s predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight. Second, we want incentive compatibility, a guarantee that each expert’s best strategy is to report his true beliefs about the realization of each event. To achieve this goal, we build on the literature on wagering mechanisms, a type of multi-agent scoring rule. We provide algorithms that achieve no regret and incentive compatibility for myopic experts for both the full and partial information settings. In experiments on datasets from FiveThirtyEight, our algorithms have regret comparable to classic no-regret algorithms, which are not incentive-compatible. Finally, we identify an incentive-compatible algorithm for forward-looking strategic agents that exhibits diminishing regret in practice.
Distortion Agnostic Deep Watermarking

Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference with the original image. Recently, deep learning-based methods achieved impressive results in both visual quality and message payload under a wide variety of image distortions. However, these methods all require differentiable models for the image distortions at training time, and may generalize poorly to unknown distortions. This is undesirable since the types of distortions applied to watermarked images are usually unknown and non-differentiable. In this paper, we propose a new framework for distortion-agnostic watermarking, where the image distortion is not explicitly modeled during training. Instead, the robustness of our system comes from two sources: adversarial training and channel coding. Compared to training on a fixed set of distortions and noise levels, our method achieves comparable or better results on distortions available during training, and better performance overall on unknown distortions.
Improving Confidence Estimates for Unfamiliar Examples

Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with relevant but unfamiliar examples. A classifier we trained to recognize gender is 12 times more likely to be wrong with a 99% confident prediction if presented with a subject from a different age group than those seen during training. In this paper, we compare and evaluate several methods to improve confidence estimates for unfamiliar and familiar samples. We propose a testing methodology of splitting unfamiliar and familiar samples by attribute (age, breed, subcategory) or sampling (similar datasets collected by different people at different times). We evaluate methods including confidence calibration, ensembles, distillation, and a Bayesian model and use several metrics to analyze label, likelihood, and calibration error. While all methods reduce over-confident errors, the ensemble of calibrated models performs best overall, and T-scaling performs best among the approaches with fastest inference.
New hybrid polysilane/polysiloxane nanocomposites

Polysilanes are saturated organosilicon macromolecules which display polyene-like electronic properties derived from {sigma}-bond delocalization along the catenated silicon backbone. Potential commercial applications of polysilanes include display devices, NLO materials, semiconductors, photoresists and photoconductors. While Wuertz coupling of dichlorosilane monomers is currently the best synthetic route for large scale preparation of high molecular weight polysilanes, drawbacks include using stoichiometric quantities of molten alkali metal, low polymer yields and significant amounts of hazardous waste. These reaction conditions also limit one`s choice of side group to the most robust organic substituents. Titanocene- and zirconocene-catalyzed polymerization of phenylsilane under mild conditions provides an attractive route to oligophenylsilanes with degrees of polymerization approaching 40, making the resulting of oligomers amenable for many electronic applications. While only arylsilanes can be polymerized by this route, the resulting oligophenylsilane can be functionalized by free radical hydrosilation of olefins, carbonyls and imines. Substitution along the polysilane backbone with sol-gel precursors should generate soluble functionalized polymer and ought to yield, after sol-gel polymerization, materials where the chromophore is entrained within a relatively inert siloxane matrix, an attractive strategy for utilizing polysilanes under thermally demanding or photooxidative conditions. Here the authors describe the extension of this approach to include (alkoxysilyl)propyl functionalities as latent sol-gel polymerizable residues, and the preparation of a new class of hybrid organic-inorganic polysilane-siloxane materials, derived from the sol-gel polymerization of functionalized polysilanes 2a-c.
FX Intervention to Stabilize or Manipulate the Exchange Rate? Inference from Profitability

We analyze the profitability of FX swaps used by the central bank of Brazil to shed light on the rationale for FX intervention. We find that swaps are profitable in expectation, suggesting that FX intervention is used to stabilize the exchange rate in the face of temporary excessive movements rather than to manipulate it away from fundamental values. In line with this interpretation, we find that the scale of FX intervention responds to the degree of exchange rate misalignment relative to UIP conditions. We also document that intervention is more aggressive when there is less uncertainty about the medium-term level of the exchange rate and when the exchange rate is overvalued rather than undervalued.
Seasonally variant gene expression in full-term human placenta.

Seasonal exposures influence human health and development. The placenta, as a mediator of the maternal and fetal systems and a regulator of development, is an ideal tissue to understand the biological pathways underlying relationships between season of birth and later life health outcomes. Here, we conducted a differential expression (DE) analysis of season of birth in full-term human placental tissue to evaluate whether the placenta may be influenced by seasonal cues. Of the analyzed transcripts, 583 displayed DE between summer and winter births (False Discovery Rate [FDR] q < .05); among these, BHLHE40, MIR210HG, and HILPDA had increased expression among winter births (Bonferroni P < .05). Enrichment analyses of the seasonally variant genes between summer and winter births indicated overrepresentation of transcription factors HIF1A, VDR, and CLOCK, among others, and of GO term pathways related to ribosomal activity and infection. Additionally, a cosinor analysis found rhythmic expression for approximately 11.9% of all 17 664 analyzed placental transcripts. These results suggest that the placenta responds to seasonal cues and add to the growing body of evidence that the placenta acts as a peripheral clock, which may provide a molecular explanation for the extensive associations between season of birth and health outcomes.
Stability of information‐sharing alliances in a three‐level supply chain

In their recent article, Leng and Parlar (L&P) (2009) analyze information-sharing alliances in a three-level supply chain (consisting of a manufacturer, a distributor, and a retailer) that faces a nonstationary end demand. Supply chain members can share demand information, which reduces information distortion and thus decreases their inventory holding and shortage costs. We expand the results from L&P by considering dynamic (farsighted) stability concepts. We use two different allocation rules and show that under some reasonable assumptions there should always be some information sharing in this supply chain. We also identify conditions under which the retailer in a stable outcome shares his demand information with the distributor, with the manufacturer, or with both remaining supply chain members. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010
How does institutional change of democratization affect the origin and diffusion of technological innovation across countries

Abstract. The main aim of this study is to explain how institutional change, based on processes of democratization, governs the origin and diffusion of technological innovation across economies. This study suggests that institutional change, based on a progressive democratization of countries, is a driving force of inventions and adoption of usable innovations in society. Policy makers, considering the positive associations between institutional change, based on a process of democratization, and paths of technological innovation, can propose best practices directed to support a higher economic freedom in society, effective regulation, higher economic and political stability, good economic governance and higher level of education system. Overall, then, institutional change, based on democratization, is a precondition for sustaining fruitful paths of technological innovation to govern the development of economies in the presence of globalization and geographical expansion in world economic system. Keywords. Institutional change, Technological innovation, Patents, Technological change, Economic change, Social change, Regulatory quality, Rule of law, Political stability, Innovative outputs, Democracy, Democratization, Economic growth, Economic performances, Rich countries, Emerging economies. JEL. B52, F55, O17, O31, O33, O35, O43, P10, P14, P48.
Synthesis of unsymmetrical sulfamides and polysulfamides via SuFEx click chemistry

As hydrogen-bond donors and acceptors, N,N′-disubstituted sulfamides have been used in a range of applications from medicinal chemistry to anion-binding catalysis. However, compared to ureas or thioureas, the utilization of this unique moiety remains marginal, in part because of a lack of general synthetic methods to access unsymmetrical sulfamides. Specifically, polysulfamides are a virtually unknown type of polymer despite their potential utility in non-covalent dynamic networks, an intense area of research in materials science. We report herein a practical and efficient process to prepare unsymmetrical sulfamides via Sulfur(VI)-Fluoride Exchange (SuFEx) click chemistry. This process was then applied to synthesize polysulfamides. Thermal analysis showed that this family of polymers possess high thermal stability and tunable glass transition temperatures. Finally, hydrolysis studies indicated that aromatic polysulfamides could be recycled back to their constituting monomers at the end of their life cycle.
Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies.

Background Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies. Methods DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan-Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM. Results In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan-Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients. Conclusion DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.
The topology of mobility-gapped insulators

Studying deterministic operators, we define a topology on the space of mobility-gapped insulators such that topological invariants are continuous maps into discrete spaces, we prove that this is indeed the case for the integer quantum Hall effect, and lastly we show why our “insulator” condition makes sense from the point of view of the localization theory using the fractional moments method.
A posteriori error estimation for the spectral deferred correction method

Abstract The spectral deferred correction method is a variant of the deferred correction method for solving ordinary differential equations. A benefit of this method is that is uses low order schemes iteratively to produce a high order approximation. In this paper we consider adjoint-based a posteriori analysis to estimate the error in a quantity of interest of the solution. This error formula is derived by first developing a nodally equivalent finite element method to the spectral deferred correction method. The error formula is then split into various terms, each of which characterizes a different component of the error. These components may be used to determine the optimal strategy for changing the method parameters to best improve the error.
A likelihood analysis of quantile-matching transformations

Quantile matching is a strictly monotone transformation that sends the observed response values $\{y_1, . . . , y_n\}$ to the quantiles of a given target distribution. A likelihood based criterion is developed for comparing one target distribution with another in a linear-model setting.
Estimation and Inference with Trees and Forests in High Dimensions

We analyze the finite sample mean squared error (MSE) performance of regression trees and forests in the high dimensional regime with binary features, under a sparsity constraint. We prove that if only $r$ of the $d$ features are relevant for the mean outcome function, then shallow trees built greedily via the CART empirical MSE criterion achieve MSE rates that depend only logarithmically on the ambient dimension $d$. We prove upper bounds, whose exact dependence on the number relevant variables $r$ depends on the correlation among the features and on the degree of relevance. For strongly relevant features, we also show that fully grown honest forests achieve fast MSE rates and their predictions are also asymptotically normal, enabling asymptotically valid inference that adapts to the sparsity of the regression function.
Proving global optimality of ACOPF solutions

Abstract We present our latest contributions in terms of mathematical modeling and algorithm development for the global optimization of the ACOPF problem. These contributions allow us to close the optimality gap on a number of open instances in the PGLIB and NESTA benchmark libraries. This is achieved by combining valid cut generation with semidefinite programming-based bound tightening. The mathematical formulations along with the solution algorithms are implemented in the modeling framework Gravity , an open-source platform for reproducible numerical experiments.
A subset of broadly responsive Type III taste cells contribute to the detection of bitter, sweet and umami stimuli

Taste receptor cells use multiple signaling pathways to detect chemicals in potential food items. These cells are functionally grouped into different types: Type I cells act as support cells and have glial-like properties; Type II cells detect bitter, sweet, and umami taste stimuli; and Type III cells detect sour and salty stimuli. We have identified a new population of taste cells that are broadly tuned to multiple taste stimuli including bitter, sweet, sour, and umami. The goal of this study was to characterize these broadly responsive (BR) taste cells. We used an IP3R3-KO mouse (does not release calcium (Ca2+) from internal stores in Type II cells when stimulated with bitter, sweet, or umami stimuli) to characterize the BR cells without any potentially confounding input from Type II cells. Using live cell Ca2+ imaging in isolated taste cells from the IP3R3-KO mouse, we found that BR cells are a subset of Type III cells that respond to sour stimuli but also use a PLCβ signaling pathway to respond to bitter, sweet, and umami stimuli. Unlike Type II cells, individual BR cells are broadly tuned and respond to multiple stimuli across different taste modalities. Live cell imaging in a PLCβ3-KO mouse confirmed that BR cells use this signaling pathway to respond to bitter, sweet, and umami stimuli. Short term behavioral assays revealed that BR cells make significant contributions to taste driven behaviors and found that loss of either PLCβ3 in BR cells or IP3R3 in Type II cells caused similar behavioral deficits to bitter, sweet, and umami stimuli. Analysis of c-Fos activity in the nucleus of the solitary tract (NTS) also demonstrated that functional Type II and BR cells are required for normal stimulus induced expression.
A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research

We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students’ Bayesian thinking with Bayesian methods applied to ...
Is the Rise in Illicit Opioids Affecting Labor Supply and Disability Claiming Rates

This paper examines how the recent transition of the opioid crisis from prescription opioids to more prevalent misuse of illicit opioids, such as heroin and fentanyl, altered labor supply behavior and disability insurance claiming rates. We exploit differential geographic exposure to the reformulation of OxyContin, the largest reduction in access to abusable prescription opioids to date, to study the effects of substitution to illicit markets. We observe meaningful reductions in labor supply measured in terms of employment-to-population ratios, hours worked, and earnings in states more exposed to reformulation relative to those less exposed. We also find evidence of increases in disability applications and beneficiaries.
Comparison of dielectric loss in titanium nitride and aluminum superconducting resonators

Lossy dielectrics are a significant source of decoherence in superconducting quantum circuits. In this report, we model and compare the dielectric loss in bulk and interfacial dielectrics in titanium nitride (TiN) and aluminum (Al) superconducting coplanar waveguide resonators. We fabricate isotropically trenched resonators to produce a series of device geometries that accentuate a specific dielectric region's contribution to the resonator quality factor. While each dielectric region contributes significantly to loss in TiN devices, the metal–air interface dominates the loss in the Al devices. Furthermore, we evaluate the quality factor of each TiN resonator geometry with and without a post-process hydrofluoric etch and find that it reduced losses from the substrate–air interface, thereby improving the quality factor.
Dual Orbital Degeneracy Lifting in a Strongly Correlated Electron System

The local structure of ${\mathrm{NaTiSi}}_{2}{\mathrm{O}}_{6}$ is examined across its Ti-dimerization orbital-assisted Peierls transition at 210 K. An atomic pair distribution function approach evidences local symmetry breaking preexisting far above the transition. The analysis unravels that, on warming, the dimers evolve into a short range orbital degeneracy lifted (ODL) state of dual orbital character, persisting up to at least 490 K. The ODL state is correlated over the length scale spanning $\ensuremath{\sim}6$ sites of the Ti zigzag chains. Results imply that the ODL phenomenology extends to strongly correlated electron systems.
Prefetching and caching for minimizing service costs: Optimal and approximation strategies

Abstract Strategically prefetching data has been utilized in practice to improve caching performance. Apart from caching data items upon requests, they can be prefetched into the cache before requests actually occur. The caching and prefetching operations compete for the limited cache space, whose size is typically much smaller than the number of data items. A key question is how to design an optimal prefetching and caching policy, assuming that the future requests can be predicted to certain extend. This question is non-trivial even under an idealized assumption that the future requests are precisely known. To investigate this problem, we propose a cost-based service model. The objective is to find the optimal offline prefetching and caching policy that minimizes the accumulated cost for a given request sequence. By casting it as a min-cost flow problem, we are able to find the optimal policy for a data trace of length N in expected time O ( N 3 ∕ 2 ) via flow-based algorithms. However, this requires the entire trace for each request and cannot be applied in real time. To this end, we analytically characterize the optimal policy by obtaining an optimal cache eviction mechanism. We derive conditions under which proactive prefetching is a better choice than passive caching. Based on these insights, we propose a lightweight approximation policy that only exploits predictions in the near future. Moreover, the approximation policy can be applied in real time and processes the entire trace in O ( N ) expected time. We prove that the competitive ratio of the approximation policy is less than 2 . Extensive simulations verify its near-optimal performance, for both heavy and light-tailed popularity distributions.
The Challenges of Crowd Workers in Rural and Urban America

Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools.
Commercial Serology Assays Predict Neutralization Activity Against SARS-CoV-2.

Background It is unknown whether a positive serology result correlates with protective immunity against SARS-CoV-2. There are also concerns regarding the low positive predictive value of SARS-CoV-2 serology tests, especially when testing populations with low disease prevalence. Methods A neutralization assay was validated in a set of PCR-confirmed positive specimens and in a negative cohort. In addition, 9530 specimens were screened using the Diazyme SARS-CoV-2 IgG serology assay and all positive results (N = 164 individuals) were reanalyzed using the neutralization assay, the Roche total immunoglobin assay, and the Abbott IgG assay. The relationship between the magnitude of a positive SARS-CoV-2 serology result and neutralizing activity was determined. Neutralizing antibody titers (50% inhibitory dilution, ID50) were also longitudinally monitored in patients confirmed to have SARS-CoV-2 by PCR. Results The SARS-CoV-2 neutralization assay had a positive percentage agreement (PPA) of 96.6% with a SARS-CoV-2 PCR test and a negative percentage agreement (NPA) of 98.0% across 100 negative control individuals. ID50 neutralization titers positively correlated with all 3 clinical serology platforms. Longitudinal monitoring of hospitalized PCR-confirmed patients with COVID-19 demonstrated they made high neutralization titers against SARS-CoV-2. PPA between the Diazyme IgG assay alone and the neutralization assay was 50.6%, while combining the Diazyme IgG assay with either the Roche or Abbott platforms increased the PPA to 79.2 and 78.4%, respectively. Conclusions These 3 clinical serology assays positively correlate with SARS-CoV-2 neutralization activity observed in patients with COVID-19. All patients confirmed SARS-CoV-2 positive by PCR develop neutralizing antibodies.
Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data

The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these goldstandard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the “leave-one-out” validation, we find that a lighter k Nearest Neighbor $(k -$NN) model with $k =2$ can obtain an average accuracy of $0. 93 \pm 0.06$, F 1 score $0. 93 \pm 0.03$, and false positive rate (FPR) below 0.08 at 50% level of confidence, which shows the promise of this work.
High-resolution cryo-EM using beam-image shift at 200 keV

Recent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilize beam-image shift to improve throughput. Despite implementation on 300 keV cryo-EM instruments, it remains unknown how well beam-image-shift data collection affects data quality on 200 keV instruments and the extent to which aberrations can be computationally corrected. To test this, a cryo-EM data set for aldolase was collected at 200 keV using beam-image shift and analyzed. This analysis shows that the instrument beam tilt and particle motion initially limited the resolution to 4.9 A. After particle polishing and iterative rounds of aberration correction in RELION, a 2.8 A resolution structure could be obtained. This analysis demonstrates that software correction of microscope aberrations can provide a significant improvement in resolution at 200 keV.
Multilingual Jointly Trained Acoustic and Written Word Embeddings.

Acoustic word embeddings (AWEs) are vector representations of spoken word segments. AWEs can be learned jointly with embeddings of character sequences, to generate phonetically meaningful embeddings of written words, or acoustically grounded word embeddings (AGWEs). Such embeddings have been used to improve speech retrieval, recognition, and spoken term discovery. In this work, we extend this idea to multiple low-resource languages. We jointly train an AWE model and an AGWE model, using phonetically transcribed data from multiple languages. The pre-trained models can then be used for unseen zero-resource languages, or fine-tuned on data from low-resource languages. We also investigate distinctive features, as an alternative to phone labels, to better share cross-lingual information. We test our models on word discrimination tasks for twelve languages. When trained on eleven languages and tested on the remaining unseen language, our model outperforms traditional unsupervised approaches like dynamic time warping. After fine-tuning the pre-trained models on one hour or even ten minutes of data from a new language, performance is typically much better than training on only the target-language data. We also find that phonetic supervision improves performance over character sequences, and that distinctive feature supervision is helpful in handling unseen phones in the target language.
Exploring Transformers for Large-Scale Speech Recognition.

While recurrent neural networks still largely define state-of-the-art speech recognition systems, the Transformer network has been proven to be a competitive alternative, especially in the offline condition. Most studies with Transformers have been constrained in a relatively small scale setting, and some forms of data argumentation approaches are usually applied to combat the data sparsity issue. In this paper, we aim at understanding the behaviors of Transformers in the large-scale speech recognition setting, where we have used around 65,000 hours of training data. We investigated various aspects on scaling up Transformers, including model initialization, warmup training as well as different Layer Normalization strategies. In the streaming condition, we compared the widely used attention mask based future context lookahead approach to the Transformer-XL network. From our experiments, we show that Transformers can achieve around 6% relative word error rate (WER) reduction compared to the BLSTM baseline in the offline fashion, while in the streaming fashion, Transformer-XL is comparable to LC-BLSTM with 800 millisecond latency constraint.
Smooth Kuranishi atlases with isotropy

Kuranishi structures were introduced in the 1990s by Fukaya and Ono for the purpose of assigning a virtual cycle to moduli spaces of pseudoholomorphic curves that cannot be regularized by geometric methods. Their core idea was to build such a cycle by patching local finite-dimensional reductions, given by smooth sections that are equivariant under a finite isotropy group. Building on our notions of topological Kuranishi atlases and perturbation constructions in the case of trivial isotropy, we develop a theory of Kuranishi atlases and cobordisms that transparently resolves the challenges posed by nontrivial isotropy. We assign to a cobordism class of weak Kuranishi atlases both a virtual moduli cycle (a cobordism class of weighted branched manifolds) and a virtual fundamental class (a Cech homology class).
A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors

The need for application-specific design of multicore/manycore processing platforms is evident with computing systems finding use in diverse application domains. In order to tailor multicore/manycore processors for application specific requirements, a multitude of processor design parameters have to be tuned accordingly which involves rigorous and extensive design space exploration over large search spaces. In this paper, we propose an efficient methodology for design space exploration. We evaluate our methodology over two search spaces small and large, using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH-2 benchmarks. For the smaller design space, we compare results obtained from our design space exploration methodology with results obtained from fully exhaustive search. The results show that solution quality obtained from our methodology are within 1.35 - 3.69 percent of the results obtained from fully exhaustive search while only exploring 2.74 - 3 percent of the design space. For larger design space, we compare solution quality of different results obtained by varying the number of tunable processor design parameters included in the exhaustive search phase of our methodology. The results show that including more number of tunable parameters in the exhaustive search phase of our methodology greatly improves solution quality.
Applications of small-scale quantum ergodicity in nodal sets

The goal of this article is to draw new applications of small-scale quantum ergodicity in nodal sets of eigenfunctions. We show that if quantum ergodicity holds on balls of shrinking radius r ( λ ) → 0 then one can achieve improvements on the recent upper bounds of Logunov (2016) and Logunov and Malinnikova (2016) on the size of nodal sets, according to a certain power of r ( λ ) . We also show that the doubling estimates and the order-of-vanishing results of Donnelly and Fefferman (1988, 1990) can be improved. Due to results of Han (2015) and Hezari and Riviere (2016), small-scale QE holds on negatively curved manifolds at logarithmically shrinking rates, and thus we get logarithmic improvements on such manifolds for the above measurements of eigenfunctions. We also get o ( 1 ) improvements for manifolds with ergodic geodesic flows. Our results work for a full density subsequence of any given orthonormal basis of eigenfunctions.
Linear Network Coding Over Rings – Part II: Vector Codes and Non-Commutative Alphabets

In Part I, we studied linear network coding over finite commutative rings and made comparisons to the well-studied case of linear network coding over finite fields. Here, we consider the more general setting of linear network coding over finite (possibly non-commutative) rings and modules. We prove the following results regarding the linear solvability of directed acyclic networks over various finite alphabets. For any network, the following are equivalent: (i) vector linear solvability over some field, (ii) scalar linear solvability over some ring, and (iii) linear solvability over some module. Analogously, the following are equivalent: (a) scalar linear solvability over some field, (b) scalar linear solvability over some commutative ring, and (c) linear solvability over some module whose ring is commutative. Whenever any network is linearly solvable over a module, a smallest such module arises in a vector linear solution for that network over a field. If a network is scalar linearly solvable over some non-commutative ring but not over any commutative ring, then such a non-commutative ring must have size at least 16, and for some networks, this bound is achieved. An infinite family of networks is demonstrated, each of which is scalar linearly solvable over some non-commutative ring but not over any commutative ring. Whenever $p$ is prime and $1 \le k \le 6$ , if a network is scalar linearly solvable over some ring of size $p^{k}$ , then it is also $k$ -dimensional vector linearly solvable over the field $ \mathrm {GF}(p)$ , but the converse does not necessarily hold. This result is extended to all $k\ge 1$ when the ring is commutative.
HST-COS Observations of AGNs. III. Spectral Constraints in the Lyman Continuum from Composite COS/G140L Data

The rest-frame ultraviolet (UV) spectra of active galactic nuclei (AGNs) are important diagnostics of both accretion disk physics and their contribution to the metagalactic ionizing UV background. Though the mean AGN spectrum is well characterized with composite spectra at wavelengths greater than 912 Angstroms, the shorter-wavelength extreme-UV (EUV) remains poorly studied. In this third paper in a series on the spectra of AGNs, we combine 11 new spectra taken with the Cosmic Origins Spectrograph on the Hubble Space Telescope with archival spectra to characterize the typical EUV spectral slope of AGNs from $\lambda_{\rm rest}\sim 850~{\rm Angstroms}$ down to $\lambda_{\rm rest}\sim 425~{\rm Angstroms}$. Parameterizing this slope as a power law, we obtain $F_\nu\propto \nu^{ -0.72\pm 0.26}$, but we also discuss the limitations and systematic uncertainties of this model. We identify broad emission features in this spectral region, including emission due to ions of O, Ne, Mg, and other species, and we limit the intrinsic HeI 504 Angstrom photoelectric absorption edge opacity to $\tau_{\rm HeI}<0.047$.
ON THE MAGNITUDE AND INTRINSIC VOLUMES OF A CONVEX BODY IN EUCLIDEAN SPACE

Magnitude is an isometric invariant of metric spaces inspired by category theory. Recent work has shown that the asymptotic behavior under rescaling of the magnitude of subsets of Euclidean space is closely related to intrinsic volumes. Here we prove an upper bound for the magnitude of a convex body in Euclidean space in terms of its intrinsic volumes. The result is deduced from an analogous known result for magnitude in $\ell_1^N$, via approximate embeddings of Euclidean space into high-dimensional $\ell_1^N$ spaces. As a consequence, we deduce a sufficient condition for infinite-dimensional subsets of a Hilbert space to have finite magnitude. The upper bound is also shown to be sharp to first order for an odd-dimensional Euclidean ball shrinking to a point; this complements recent work investigating the asymptotics of magnitude for large dilatations of sets in Euclidean space.
Quasimodular moonshine and arithmetic connections

We prove the existence of a module for the largest Mathieu group, whose trace functions are weight two quasimodular forms. Restricting to the subgroup fixing a point, we see that the integrality of these functions is equivalent to certain divisibility conditions on the number of $\mathbb{F}_p$ points on Jacobians of modular curves. Extending such expressions to arbitrary primes, we find trace functions for modules of cyclic groups of prime order with similar connections. Moreover, for cyclic groups, we give an explicit vertex operator algebra construction whose trace functions are given only in terms of weight two Eisenstein series.
Optimal extension of Lipschitz embeddings in the plane

We prove that every bi-Lipschitz embedding of the unit circle into the plane can be extended to a bi-Lipschitz map of the unit disk with linear bounds on the constants involved. This answers a question raised by Daneri and Pratelli. Furthermore, every Lipschitz embedding of the circle extends to a Lipschitz homeomorphism of the plane, again with a linear bound on the constant.
History and evidence regarding hydrostatic shock.

Abstract This paper reviews the history and evidence related to remote wounding effects of ballistic pressure waves imparted to tissue by the impact of a bullet. Such remote effects are often referred to as hydraulic or hydrostatic shock. In spite of considerable published evidence and a long history, some medical professionals continue to regard the ability of a bullet to injure tissue that is not directly crushed or stretched as mythical (Jandial R, Reichwage B, Levy M, Duenas V, Sturdivan L. Ballistics for the neurosurgeon. Neurosurgery. 2008:62:472-480.) Early references to these effects date to the 19 th century; however, compelling experimental support and medical findings in human case studies did not become available until the late 20 th and early 21 st century. Keywords : ballistics, traumatic brain injury, TBI, blast wave, ballistic pressure wave Without citing data in support for the claim, the otherwise excellent review paper,
RADIATING BONDI AND COOLING SITE FLOWS

Steady accretion of a radiating gas onto a central mass point is described and compared to classic Bondi accretion. Radiation losses are essential for accretion flows to be observed. Unlike Bondi flows, radiating Bondi flows pass through a sonic point at a finite radius and become supersonic near the center. The morphology of all radiating flows is described by a single dimensionless parameter proportional to where Ts is the gas temperature at the sonic point. In radiating Bondi flows the relationship between the mass accretion rate and central mass, with p ~ 1, differs significantly from the quadratic dependence in classical Bondi flows, . Mass accretion rates onto galaxy or cluster-centered black holes estimated from traditional and radiating Bondi flows are significantly different. In radiating Bondi flows the gas temperature increases at large radii, as in the cores of many galaxy groups and clusters, allowing radiating Bondi flows to merge naturally with gas arriving from their cluster environments. Some radiating flows cool completely before reaching the center of the flow, and this also occurs in cooling site flows, in which there is no central gravitating mass.
Voronoi Tessellation and Non-parametric Halo Concentration

We present and test TesseRACt, a non-parametric technique for recovering the concentration of simulated dark matter halos using Voronoi tessellation. TesseRACt is tested on idealized N-body halos that are axisymmetric, triaxial, and contain substructure and compared to traditional least-squares tting as well as two non-parametric techniques that assume spherical symmetry. TesseRACt recovers halo concentrations within 0.3% of the true value regardless of whether the halo is spherical, axisymmetric, or triaxial. Traditional tting and non-parametric techniques that assume spherical symmetry can return concentrations that are systematically o by as much as 10% from the true value for nonspherical halos. TesseRACt also performs signicantly better when there is substructure present outside 0:5R200. Given that cosmological halos are rarely spherical and often contain substructure, we discuss implications for studies of halo concentration in cosmological N-body simulations including how choice of technique for measuring concentration might bias scaling relations. Subject headings: galaxies: fundamental parameters | galaxies: halos | methods: numerical
Could GRB 060614 and Its Presumed Host Galaxy Be a Chance Superposition

The lack of an observed supernova associated with GRB 060614 appears to require a new paradigm for the formation of (a subset of) long-duration GRBs. This requirement is based on the presumed low redshift of the burst, which was inferred from the spatial coincidence of the afterglow with a z = 0.125 galaxy. We explore the possibility that this low-redshift galaxy is a chance superposition along the line of sight to GRB 060614. We examine the galaxy distribution of the field of GRB 060614 and find that the probability of a chance association with a galaxy at least as bright as the putative host is only ~0.5%-1.9%. However, for the current ensemble of ≈180 Swift GRBs, it is likely that several such coincidences have occurred, and given the "nonstandard" nature of GRB 060614 it is not implausible that this is one such occurrence. Thus, the conclusion that GRB 060614 requires a revision to the formation paradigm for long-duration GRBs should be approached with caution.
ConjNLI: Natural Language Inference Over Conjunctive Sentences

Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic. Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge. Hence, we introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced. These sentences contain single and multiple instances of coordinating conjunctions ("and", "or", "but", "nor") with quantifiers, negations, and requiring diverse boolean and non-boolean inferences over conjuncts. We find that large-scale pre-trained language models like RoBERTa do not understand conjunctive semantics well and resort to shallow heuristics to make inferences over such sentences. As some initial solutions, we first present an iterative adversarial fine-tuning method that uses synthetically created training data based on boolean and non-boolean heuristics. We also propose a direct model advancement by making RoBERTa aware of predicate semantic roles. While we observe some performance gains, ConjNLI is still challenging for current methods, thus encouraging interesting future work for better understanding of conjunctions.
Very Low-energy Supernovae: Light Curves and Spectra of Shock Breakout

Author(s): Lovegrove, E; Woosley, SE; Zhang, W | Abstract: The brief transient emitted as a shock wave erupts through the surface of a pre-supernova star carrying information about the stellar radius and explosion energy. Here, the CASTRO code, which treats radiation transport using multigroup flux-limited diffusion, is used to simulate the light curves and spectra of shock breakout in very low-energy supernovae (VLE SNe), explosions in giant stars with final kinetic energy much less than 1051 erg. VLE SN light curves, computed here with the KEPLER code, are distinctively faint, red, and long-lived, making them challenging to find with transient surveys. The accompanying shock breakouts are brighter, though briefer, and potentially easier to detect. Previous analytic work provides general guidance, but numerical simulations are challenging, due to the range of conditions and lack of equilibration between color and effective temperatures. We consider previous analytic work and extend discussions of color temperature and opacity to the lower energy range explored by these events. Since this is the first application of the CASTRO code to shock breakout, test simulations of normal energy shock breakout of SN 1987A are carried out and compared with the literature. A set of breakout light curves and spectra are then calculated for VLE SNe with final kinetic energies in the range 1047 - 1050erg for red supergiants with main-sequence masses of 15 and 25 M⊙. The importance of uncertainties in stellar atmosphere model, opacity, and ambient medium is discussed, as are observational prospects with current and forthcoming missions.
Efficient Construction of Mock Catalogs for Baryon Acoustic Oscillation Surveys

Precision measurements of the large scale structure of the Universe require large numbers of high fidelity mock catalogs to accurately assess, and account for, the presence of systematic effects. We introduce and test a scheme for generating mock catalogs rapidly using suitably derated N-body simulations. Our aim is to reproduce the large scale structure and the gross properties of dark matter halos with high accuracy, while sacrificing the details of the halo's internal structure. By adjusting global and local time-steps in an N-body code, we demonstrate that we recover halo masses to better than 0.5% and the power spectrum to better than 1% both in real and redshift space for k=1hMpc−1, while requiring a factor of 4 less CPU time. We also calibrate the redshift spacing of outputs required to generate simulated light cones. We find that outputs separated by Δ z=0.05 allow us to interpolate particle positions and velocities to reproduce the real and redshift space power spectra to better than 1% (out to k=1hMpc−1). We apply these ideas to generate a suite of simulations spanning a range of cosmologies, motivated by the Baryon Oscillation Spectroscopic Survey (BOSS) but broadly applicable to future large scale structure surveys including eBOSS and DESI. As an initial demonstration of the utility of such simulations, we calibrate the shift in the baryonic acoustic oscillation peak position as a function of galaxy bias with higher precision than has been possible so far. This paper also serves to document the simulations, which we make publicly available.
On the problem of initial conditions for inflation

I review the present status of the problem of initial conditions for inflation and describe several ways to solve this problem for many popular inflationary models, including the recent generation of the models with plateau potentials favored by cosmological observations.