cssi/SciSciGPT-SciSciNet huggingface dataset:
Brick Manifolds and Toric Varieties of Brick Polytopes

Bott-Samelson varieties are a twisted product of $\mathbb{C}\mathbb{P}^1$'s with a map into $G/B$. These varieties are mostly studied in the case in which the map into $G/B$ is birational to the image; however in this paper we study a fiber of this map when it is not birational. We prove that in some cases the general fiber, which we christen a brick manifold, is a toric variety. In order to do so we use the moment map of a Bott-Samelson variety to translate this problem into one in terms of the "subword complexes" of Knutson and Miller. Pilaud and Stump realized certain subword complexes as the dual of the boundary of a polytope which generalizes the brick polytope defined by Pilaud and Santos. For a nice family of words, the brick polytope is the generalized associahedron realized by Hohlweg, Lange and Thomas. These stories connect in a nice way: we show that the moment polytope of the brick manifold is the brick polytope. In particular, we give a nice description of the toric variety of the associahedron. We give each brick manifold a stratification dual to the subword complex. In addition, we relate brick manifolds to Brion's resolutions of Richardon varieties.
Discovering effect modification in an observational study of surgical mortality at hospitals with superior nursing

Summary There is effect modification if the magnitude or stability of a treatment effect varies systematically with the level of an observed covariate. A larger or more stable treatment effect is typically less sensitive to bias from unmeasured covariates, so it is important to recognize effect modification when it is present. We illustrate a recent proposal for conducting a sensitivity analysis that empirically discovers effect modification by exploratory methods but controls the familywise error rate in discovered groups. The example concerns a study of mortality and use of intensive care units in 23715 matched pairs of two Medicare patients, one of whom underwent surgery at a hospital that had been identified for superior nursing; the other at a conventional hospital. The pairs were matched exactly for 130 four-digit ninth international classification of diseases surgical procedure codes and balanced 172 observed covariates. The pairs were then split into five groups of pairs by the classification and regression trees method in its effort to locate effect modification. The evidence of a beneficial effect of magnet hospitals on mortality is least sensitive to unmeasured biases in a large group of patients undergoing quite serious surgical procedures, but in the absence of other life-threatening conditions, such as a comorbidity of congestive heart failure or an emergency admission leading to surgery.
Intermolecular [2 + 2 + 2] Cycloaddition Reactions of Alkynes and Alkenes Mediated by Cobalt: X-ray Crystal Structures of Two Isomeric (η5-Cyclopentadienyl)(η4-1,3-cyclohexadiene)cobalt Complexes

Les deux composes cristallisent dans le systeme triclinique, groupe d'espace P1 et leur structure est affinee jusqu'a R=0,041
AI researchers, Video Games are your friends!

Artificial intelligence and games go way back. At least to Turing, who re-invented the Minimax algorithm to play Chess even before he had a computer, and to Samuel, who invented a predecessor of TD-learning in order to build a Checkers-playing program in the 1950s. Games are important for AI because they are designed to challenge and train human cognitive capabilities, and are thus uniquely relevant benchmark problems. They are also uniquely convenient benchmark problems, as they allow unbiased comparison between algorithms and can be executed thousands of times fast than realtime. But one should also not forget the financial clout of the games industry and games' appeal for new students. While research on board games such as Chess and Go has been part of AI research since its inception, the last decade has seen the rise of a research community around AI for video games, and not only for playing them. In this talk I will outline some of the most important trends in recent years. One is General Video Game Playing: developing controllers that can learn to play not just a single game, but a large variety variety of them. Another is Procedural Content Generation, where AI algorithms are used to generate content for games, or even design the games themselves. Yet another trend is AI-assisted design tools, which provide the game designer with instant feedback and suggestions and thus scaffolds the game design process. These research topics inform each other, with general video game playing algorithms being important for procedural content generation and AI-assisted design tools. Finally, I will try to convince you that your own research is important for this endeavor and that you should consider steering your research towards AI for games.
Efficient scalable speech compression for scalable speech recognition.

We propose a scalable recognition system for reducing recognition complexity. Scalable recognition can be combined with scalable compression in a distributed speech recognition (DSR) application to reduce both the computational load and the bandwidth requirement at the server. A low complexity preprocessor is used to eliminate the unlikely classes so that the complex recognizer can use the reduced subset of classes to recognize the unknown utterance. It is shown that by using our system it is fairly straightforward to trade-off reductions in complexity for performance degradation. Results of preliminary experiments using the TI-46 word digit database show that the proposed scalable approach can provide a 40% speed up, while operating under 1.05 kbps, compared to the baseline recognition using uncompressed speech.
Conditions for the Yoneda algebra of a local ring to be generated in low degrees

Abstract The powers m n of the maximal ideal m of a local Noetherian ring R are known to satisfy certain homological properties for large values of n. For example, the homomorphism R → R / m n is Golod for n ≫ 0 . We study when such properties hold for small values of n, and we make connections with the structure of the Yoneda Ext algebra, and more precisely with the property that the Yoneda algebra of R is generated in degrees 1 and 2. A complete treatment of these properties is pursued in the case of compressed Gorenstein local rings.
GenVisR: Genomic Visualizations in R

Summary: Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor. Availability and Implementation: GenVisR is an R package available via Bioconductor (https://bioconductor.org/packages/GenVisR) under GPLv3. Support is available via GitHub (https://github.com/griffithlab/GenVisR/issues) and the Bioconductor support website. Contacts: ude.ltsuw@htiffirgibo or ude.ltsuw@tiffirgm Supplementary information: Supplementary data are available at Bioinformatics online.
Accelerated Continuous Conditional Random Fields for load forecasting

Enabling commercial buildings to use energy in a flexible way, such as reducing usage during consumption peak hours, is of crucial importance, not only for preventing the disruptions of the utility grid, but also for containing buildings' rapidly growing energy cost. Such smart energy consumption, however, heavily relies on accurate short-term energy load forecasting, such as hourly predictions for the next n (n ≥ 2) hours. To attain sufficient accuracy, we treat such multi-steps ahead regression task as a sequence labeling (regression) problem, and adopt the Continuous Conditional Random Fields (CCRF) [1] to explicitly model these interconnected outputs.
Private Proximity Detection for Convex Polygons

Proximity detection is an emerging technology in Geo-Social Networks that notifies mobile users when they are in proximity. Nevertheless, users may be unwilling to participate in such applications if they are required to disclose their exact locations to a centralized server and/or their social friends. To this end, private proximity detection protocols allow any two parties to test for proximity while maintaining their locations secret. In particular, a private proximity detection query returns only a boolean result to the querier and, in addition, it guarantees that no party can derive any information regarding the other party's location. However, most of the existing protocols rely on simple grid decompositions of the space and assume that two users are in proximity when they are located inside the same grid cell. In this paper, we extend the notion of private proximity detection, and propose a novel approach that allows a mobile user to define an arbitrary convex polygon on the map and test whether his friends are located therein. Our solution employs a secure two-party computation protocol and is provably secure. We implemented our method on handheld devices and illustrate its efficiency in terms of both computational and communication cost.
Arthroscopic treatment of femoroacetabular impingement.

Summary:Femoroacetabular impingement (FAI) is condition that occurs commonly in young adults and middle age that causes hip pain felt mostly in the groin. X-rays often appear “normal” to the untrained observer, however, on careful analysis an anterior to lateral bump may be visualized at the head ne
LUNATE AND PERILUNATE INJURIES: AN ALTERNATIVE MANAGEMENT STRATEGY

Lunate or perilunate dislocations are common carpal injuries. Current treatment of these injuries by repair or reconstruction of intra-carpal ligaments is largely based on Mayfield9s description of sequential failure of these ligaments. We do treat significant number of these injuries. We have observed that dorsal wrist capsule is attached to dorsal aspect of proximal carpal row and its interosseous ligaments by vertically oriented identifiable fibres. This can be seen as carpal bones suspended from dorsal capsule, akin to cloths suspended from a washing line. We have also observed that in lunate or perilunate dislocations, dorsal capsule is peeled off from the dorsal aspect of lunate and distal radius, similar to a Bankart lesion in the shoulder. We believe that dorsal capsule plays a bigger role in the stabilising mechanism of carpal bone than the intercarpal ligaments. It has not been described before. We dissected three cadaveric wrists and found vertical fibres running from dorsal wrist capsule/ligaments to the dorsal components of the scapholunate and lunotriquetralinterosseous ligaments. We have modified the Mayo approach to dorsal wrist capsule and use suture anchors to attach dorsal capsule/ligaments to scaphoid, lunate and triquetrum rather than repairing intra-carpal ligament. We have used this technique in 26 patients so far. Follow up for more than 4 years have shown satisfactory results and no significant recurrence of instability. We present a novel, so far unreported, method of repairing the intracarpal injuries, using the dorsal capsule/ligaments, based on anatomic and intra-operative observations.
The relationship between school mobility and students in foster care earning a high school credential

Abstract This study examined the relationship between school mobility for Colorado students in foster care and educational attainment outcomes, specifically earning high school diploma, a high school equivalency diploma (e.g., through examination such as a GED), or exiting the K-12 system without a credential. Multinomial logistic regression was utilized to analyze the predictive role of school mobility related to high school educational attainment within a statewide sample of four cohorts of students who experienced out-of-home placements during high school. Results indicated that students in the foster care cohorts changed public schools an average of 3.46 times during their first four years of high school. As the average number of school changes increased, the odds of earning a high school diploma decreased and the odds of earning an equivalency diploma (e.g., GED) or exiting without a credential increased. Grade level analysis suggests that educational stability in ninth and twelfth grades may be particularly important to closing the high school graduation gap for students in foster care. A more comprehensive child welfare profile that includes frequency of residential moves, types of placements, and mental or behavioral health indicators was not included in analyses, and this limitation must be considered when using the results of this study to inform policy or practice.
System of 3-D Printed Components for the Rapid Prototyping of Legged Robots

A system of components designed for the rapid prototyping of different robot body plans for legged robots is described in this chapter. The components are designed and fabricated using 3-D printing technology. As such, they constitute a low-cost way to experiment with legged robots for researchers, practitioners, and students. As legged robots become more common, there will be a need for new designs and new intelligent behaviors. A system of components that facilitates experimentation can play an important role in this development. And because these components are 3-D printed, they can easily be shared and fabricated by others.
Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements

Abstract. Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. This study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network. Data from this study are available at http://dx.doi.org/10.15486/NGT/1279968
From science to technology: The value of knowledge from different energy research institutions

Abstract Expansion of public energy R&D budgets continues to be a key component of climate policy. Using an original data set of both scientific articles and patents pertaining to three alternative energy technologies (biofuels, solar and wind energy), this paper provides new evidence on the flows of knowledge between university, private sector, and government research. Better understanding of the value of knowledge from these institutions can help decision makers target R&D funds where they are most likely to be successful. I use citation data from both scientific articles and patents to answer two questions. First, what information is most useful to the development of new technology? Does high quality science lead to applied technology development? I find that this is the case, as those articles most highly cited by other scientific articles are also more likely to be cited by future patents. Second, which institutions produce the most valuable research? Are there differences across technologies? Research performed at government institutions appears to play an important translational role linking basic and applied research, as government articles are more likely to be cited by patents than any other institution, including universities. Universities play a less important role in wind research than for solar and biofuels, suggesting that wind energy research is at a more applied stage where commercialization and final product development is more important than basic research.
Audit Committee Accounting Expertise, Analyst Following, and Market Liquidity:

We study the relation between audit committee accounting expertise, analyst following, and market liquidity. Our main results indicate that analyst following increases subsequent to the appointment of an accounting expert to the audit committee. We also provide evidence that accrual quality, as opposed to audit quality or management earnings forecasts, is the channel through which accounting expertise increases analyst following and improves analyst forecast properties. We also show that audit committee accounting expertise is related to higher trading volume and lower liquidity risk, supporting incentives for greater analyst following. Our study extends prior literature by providing evidence that audit committee accounting expertise enhances firms’ information environment beyond the effects it has on financial reporting quality or analysts’ forecast properties. Our study also complements the literature on determinants of analyst following and market liquidity, both of which are related to cost of capital...
Counting Better — The Limits and Future of Quality-Based Compensation

A Seattle medical group found that although performance improved on the quality metrics it emphasized, those metrics didn't reflect the essence of patient care and the focus felt stifling to clinicians. So the group began charting a different path toward higher-value care.
Predictors of persistent diarrhea in norovirus enteritis after solid organ transplantation

Solid organ transplant recipients may develop protracted diarrheal illness from norovirus. We performed a retrospective chart review between January 2010 and April 2014 to identify predictors of persistent diarrhea in transplant recipients with norovirus enteritis.152 solid organ transplant recipients with mean age of 31.5 years (SD 23.1) were included: 43.4% male, 34.2% pediatric patients. Allograft types were abdominal 136 (89.5%) [kidney (39.5%), liver-small bowel (23%), other (27%)] and thoracic 16 (10.5%). The median time to diagnosis of first norovirus enteritis episode from date of transplantation was 1.7 (0.3 – 5.3) years. At time of presentation, diarrhea was present in 141 (93%). Thirty percent had persistent diarrhea at 2 weeks. Hospitalization was required for treatment in 121 (80%) of episodes with the mean length of stay of 10 ±15.2 days. Most (91%) infections were due to norovirus genogroup II, and gastrointestinal co-infections were seen in 23 (19%) norovirus enteritis episodes. Nausea at time of diagnosis (p=0.002) and CMV infection in the preceding 90 days (p=0.036) were identified as independent risk factors for persistent diarrhea using univariate and multivariable logistic regression. Our study shows that nausea on presentation and prior cytomegalovirus infection were associated with persistent diarrhea in patients with norovirus enteritis. This article is protected by copyright. All rights reserved.
Experimental Studies of Concrete Activation at the National Ignition Facility Using the Rotating Target Neutron Source

Two samples of concrete for possible use as a structural support material of the National Ignition Facility Target Chamber were irradiated with 14 MeV neutrons from the Rotating Target Neutron Source at U.C. Berkeley. Dose rate measurements were taken, using a Victoreen 450P pressurized ion chamber, from the front and rear faces of each concrete slug in order to compare to simulated dose rates calculated by the codes ACAB and TART. The results of the experiment have validated the results of the code calculations. Construction material decisions were made based on the agreement of experiment with simulation.
Early outcomes of patellofemoral replacement in isolated patellofemoral arthritis: results from the Trent region, UK

Introduction The optimal treatment for isolated patello-femoral arthritis is unclear. Patello-femoral arthroplasty (PFA) may offer superior knee function in isolated patello-femoral osteoarthritis compare to TKA. The literature is controversial for patient outcomes in PFA. Some reports showed improved outcomes while others were disappointing. We assessed our outcomes to try to identify causes for poor outcomes. Methods The Trent Arthroplasty was established in 1990 to collect prospective data on knee arthroplasty surgery. Data is entered by surgeons at the time of surgery, with patient consent. PFA constitute less than 1% of the arthroplasties performed in this region. Patients were sent self-administered outcome forms 1 year post-op. Re-operation and revision procedures were reported. 334 PFA have been registered from 17 hospitals. 79% were female patients with 43% of the patients aged 55 years or less, suggesting dysplasia as the cause of their osteoarthritis. Age range 28–94 yrs (SD 11.8 yrs). The implants were Stryker Avon 236, Corin Leicester 47, Link Lubinus 24, Smith & Nephew Journey 10, DePuy LCS 7, Wright FPV 2, other 8. Results 18% of patients stated that they were dissatisfied 1 year post-op (7.9% for TKA) and 12% were unsure. Pain was the cause of dissatisfaction in two-thirds of the dis-satisfied patients. Fifty-eight of the 334 implants have been revised, 45 to a TKA. Patellar button was revised in 70% of cases. Revision was due to progression of arthritis in 29, continuing pain in 9, and aseptic loosening of the patella in 7. Conclusion Results of patello-femoral arthroplasty appear to be disappointing. This may be due to patient selection or surgery being technically demanding. Given that this surgery is performed infrequently, surgeons should be cautious when considering isolated patello-femoral replacement and should inform the patient of patient reported outcomes. Therefore, non-arthroplasty surgery or a TKA should be considered.
An Interaction with Ewing’s Sarcoma Breakpoint Protein EWS Defines a Specific Oncogenic Mechanism of ETS Factors Rearranged in Prostate Cancer

Summary More than 50% of prostate tumors have a chromosomal rearrangement resulting in aberrant expression of an oncogenic ETS family transcription factor. However, mechanisms that differentiate the function of oncogenic ETS factors expressed in prostate tumors from non-oncogenic ETS factors expressed in normal prostate are unknown. Here, we find that four oncogenic ETS (ERG, ETV1, ETV4, and ETV5), and no other ETS, interact with the Ewing's sarcoma breakpoint protein, EWS. This EWS interaction was necessary and sufficient for oncogenic ETS functions including gene activation, cell migration, clonogenic survival, and transformation. Significantly, the EWS interacting region of ERG has no homology with that of ETV1, ETV4, and ETV5. Therefore, this finding may explain how divergent ETS factors have a common oncogenic function. Strikingly, EWS is fused to various ETS factors by the chromosome translocations that cause Ewing's sarcoma. Therefore, these findings link oncogenic ETS function in both prostate cancer and Ewing's sarcoma.
(2, 2) superconformal bootstrap in two dimensions

We find a simple relation between two-dimensional BPS N=2 superconformal blocks and bosonic Virasoro conformal blocks, which allows us to analyze the crossing equations for BPS 4-point functions in unitary (2, 2) superconformal theories numerically with semidefinite programming. We constrain gaps in the non-BPS spectrum through the operator product expansion of BPS operators, in ways that depend on the moduli of exactly marginal deformations through chiral ring coefficients. In some cases, our bounds on the spectral gaps are observed to be saturated by free theories, by N=2 Liouville theory, and by certain Landau-Ginzburg models.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency.
Electrical breakdown in composite insulating systems: Liquid-solid interface parallel to field

A study is presented which identifies some of the characteristics of electrical breakdown at a liquid-solid interface. The primary emphasis is on paper interfaces in transformer oil and studies are performed using impulse and using 60 Hz waveforms. The interfaces are parallel to the electric field. Several cylindrically-symmetric electrode geometries are employed ranging from plane-plane to sphere-sphere. The electrode system is split along the symmetry axis to hold the paper parallel to the field. Among the conclusions drawn from this study are the following: First, the breakdown frequently occurs at positions other than the interface for both impulse and 60 Hz waveforms. Second, no significant difference is identified between the breakdown voltage with or without an interface in the system or between the breakdown voltage when a breakdown occurs at an interface and when it does not.
Coupling immunity and programmed cell suicide in prokaryotes: Life-or-death choices

Host-pathogen arms race is a universal, central aspect of the evolution of life. Most organisms evolved several distinct yet interacting strategies of anti-pathogen defense including resistance to parasite invasion, innate and adaptive immunity, and programmed cell death (PCD). The PCD is the means of last resort, a suicidal response to infection that is activated when resistance and immunity fail. An infected cell faces a decision between active defense and altruistic suicide or dormancy induction, depending on whether immunity is "deemed" capable of preventing parasite reproduction and consequent infection of other cells. In bacteria and archaea, immunity genes typically colocalize with PCD modules, such as toxins-antitoxins, suggestive of immunity-PCD coupling, likely mediated by shared proteins that sense damage and "predict" the outcome of infections. In type VI CRISPR-Cas systems, the same enzyme that inactivates the target RNA might execute cell suicide, in a case of ultimate integration of immunity and PCD.
PARSEC-process analysis with recipe support for etcher control

The authors describe PARSEC, a system developed to automate processing, perform endpoint signal analysis, and monitor wafer movement within the plasma area. The system provides automatic downloading of process recipes, detection of defined process problems by the automated analysis of endpoint signals, automatic archiving of these signals, and automated data logging for increased lot-tracking efficiency. The system hardware, software, support tools, implementation, performance and results are described. >
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi.

Summary The minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC ('EPIC') array from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 ('450k') and EPIC platforms. We introduce the single-sample Noob ( ssNoob ) method, a normalization procedure suitable for incremental preprocessing of individual methylation arrays and conclude that this method should be used when integrating data from multiple generations of Infinium methylation arrays. We show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data. Availability and implementation The minfi package version 1.19.12 or higher is available for all platforms from the Bioconductor project. Contact khansen@jhsph.edu. Supplementary information Supplementary data are available at Bioinformatics online.
In Silico Discovery of a Protein-Protein Interaction Inhibitor for Influenza Viruses

The Influenza virus poses a significant public health threat. Frequent mutations make the virus a difficult target for antiviral development. Amantadine and rimantadine are now ineffective against most circulating strains due to point mutations in the M2 channel, and neuraminidase inhibitors are also vulnerable to viral resistance. This highlights the need for the discovery of antivirals that inhibit infection by a new mechanism. We set out to design antivirals that would block influenza replication by inhibiting the interaction of Polymerase Basic 1 (PB1), a subunit of the viral polymerase complex, and Ran-Binding Protein 5 (Ran BP5), a host importin, with a small molecule that would bind to PB1. It has been shown that PB1 interacts with Ran BP5 for nuclear import, a necessary step in viral replication. Compounds that might block nuclear import of the polymerase complex were identified using high-throughput virtual screening coupled with in vitro validation assays. The Asinex protein-protein interactions library was docked with Glide and Autodock Vina to the PB1 subunit of 4WSB, the trimeric polymerase complex of bat influenza. The proposed binding site was chosen due to its proximity to the bipartite nuclear localization signal (NLS), and the top hits were rescreened using a higher exhaustiveness to confirm the results. Four compounds were chosen and tested in cell culture for inhibition of viral replication with an immunofluorescence assay and for cytotoxicity with a vital dye uptake assay. A lead compound was identified that has an EC50 of 13.89 µM against A/California/07/2009 and an EC50 of 16.19 µM against A/WS/33. This compound also showed some activity against B/USSR/69 (Brigit) with an EC50 of 37.52 µM. This lead compound is amenable to medicinal optimization.
Infrastructure and general purpose technologies: a technology flow framework

Studies of economic growth often refer to “general purpose technology” (GPT), “infrastructure,” and “openness” as keys to improving productivity. Some GPTs, like railroads and the Internet, fit common notions of infrastructure and spawn debates about openness, such as network neutrality. Other GPTs, like the steam engine and the computer, seem to be in a different group that is more modular and open by nature. Big data, artificial intelligence, and various emerging smart technological assemblages have been described both as GPTs and infrastructure. We present a technology flow framework that clarifies when a GPT is implemented through infrastructure, provides a basis for policy analysis, and defines empirical research questions. On the demand side, all GPTs—whether implemented through infrastructure or not—enable a wide variety of productive uses and generate substantial spillovers to the rest of the economy. On the supply side, infrastructure is different from many other implementations of GPTs; infrastructure is partially nonrival, which may complicate appropriation problems and raise congestion issues. It also exhibits tethering, meaning that different users must be physically or virtually connected for the infrastructure to function, and this makes control of its uses more feasible and more salient to policy.
The value of inferring the internal state of traffic participants for autonomous freeway driving

Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.
Preliminary Analysis of Potential Chemical Environments Inside Failed Waste Containers at the Proposed Yucca Mountain Repository

Prediction of radionuclide release rates for high-level waste requires estimates of the rates of waste form alteration and formation of secondary minerals inside the failed canister. Unsaturated repository sites may promote development of a variety of chemical environments related to two phase (liquid/ vapor) transport and temperature gradients caused by radiogenic decay. A mass balance (shell balance) approach is used to estimate the effects of dripping water, evaporation, and condensation on the waste canister and the presence of saline water inside the failed waste canister. The simplified calculations predict large variability of water chemistry over spatial scales of a few centimeters. The effects of the predicted aqueous chemistry on waste form alteration, secondary mineral formation, and radionuclide solubility are examined.
Making Sense of Bell's Theorem and Quantum Nonlocality

Bell’s theorem has fascinated physicists and philosophers since his 1964 paper, which was written in response to the 1935 paper of Einstein, Podolsky, and Rosen. Bell’s theorem and its many extensions have led to the claim that quantum mechanics and by inference nature herself are nonlocal in the sense that a measurement on a system by an observer at one location has an immediate effect on a distant entangled system (one with which the original system has previously interacted). Einstein was repulsed by such “spooky action at a distance” and was led to question whether quantum mechanics could provide a complete description of physical reality. In this paper I argue that quantum mechanics does not require spooky action at a distance of any kind and yet it is entirely reasonable to question the assumption that quantum mechanics can provide a complete description of physical reality. The magic of entangled quantum states has little to do with entanglement and everything to do with superposition, a property of all quantum systems and a foundational tenet of quantum mechanics.
Weighted Random Walk Sampling for Multi-Relational Recommendation

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be modeled as a complex network of multiple relations, not only a single relation. Recently research on multi-relational factorization and hybrid recommender models has shown that using extended meta-paths to capture additional information about both users and items in the network can enhance the accuracy of recommendations in such networks. Most of this work is focused on unweighted heterogeneous networks, and to apply these techniques, weighted relations must be simplified into binary ones. However, information associated with weighted edges, such as user ratings, which may be crucial for recommendation, are lost in such binarization. In this paper, we explore a random walk sampling method in which the frequency of edge sampling is a function of edge weight, and apply this generate extended meta-paths in weighted heterogeneous networks. With this sampling technique, we demonstrate improved performance on multiple data sets both in terms of recommendation accuracy and model generation efficiency.
Magnitude of pseudopotential localization errors in fixed node diffusion quantum Monte Carlo

Growth in computational resources has lead to the application of real space diffusion quantum Monte Carlo to increasingly heavy elements. Although generally assumed to be small, we find that when using standard techniques, the pseudopotential localization error can be large, on the order of an electron volt for an isolated cerium atom. We formally show that the localization error can be reduced to zero with improvements to the Jastrow factor alone, and we define a metric of Jastrow sensitivity that may be useful in the design of pseudopotentials. We employ an extrapolation scheme to extract the bare fixed node energy and estimate the localization error in both the locality approximation and the T-moves schemes for the Ce atom in charge states 3+ and 4+. The locality approximation exhibits the lowest Jastrow sensitivity and generally smaller localization errors than T-moves although the locality approximation energy approaches the localization free limit from above/below for the 3+/4+ charge state. We find that energy minimized Jastrow factors including three-body electron-electron-ion terms are the most effective at reducing the localization error for both the locality approximation and T-moves for the case of the Ce atom. Less complex or variance minimized Jastrows are generally less effective. Our results suggest that further improvements to Jastrow factors and trial wavefunction forms may be needed to reduce localization errors to chemical accuracy when medium core pseudopotentials are applied to heavy elements such as Ce.
A scalable method for deductive generalization in the spreadsheet paradigm

In this paper, we present an efficient method for automatically generalizing programs written in spreadsheet languages. The strategy is to do generalization through incremental analysis of logical relationships among concrete program entities from the perspective of a particular computational goal. The method uses deductive dataflow analysis with algebraic back-substitution rather than inference with heuristics, and there is no need for generalization-related dialog with the user. We present the algorithms and their time complexities and show that, because the algorithms perform their analyses incrementally, on only the on-screen program elements rather than on the entire program, the method is scalable. Performance data is presented to help demonstrate the scalability.
Oxidative Addition Complexes as Precatalysts for Cross-Coupling Reactions Requiring Extremely Bulky Biarylphosphine Ligands

In this report, we describe the application of palladium-based oxidative addition complexes (OACs) as effective precatalysts for C–N, C–O, and C–F cross-coupling reactions with a variety of (hetero)arenes. These complexes offer a convenient alternative to previously developed classes of precatalysts, particularly in the case of the bulkiest biarylphosphine ligands, for which palladacycle-based precatalysts do not readily form. The precatalysts described herein are easily prepared and stable to long-term storage under air.
Historical origins of persistent inequality in Nigeria

Horizontal inequality by ethnic group has remained remarkably persistent for wealth, education and access to certain public services in Nigeria. While there has been notable progress made towards i...
To Err is human? How typographical and orthographical errors affect perceptions of online reviewers

Abstract Consumers increasingly rely on online product reviews when making purchase decisions. However, assessing the credibility of online reviewers presents consumers with unique challenges. This paper examines how consumer perceptions of reviewer credibility are influenced by the presence and type of textual errors in the review itself. The results of an online experiment indicate that consumers’ reactions to textual errors are moderated by their general trust in others. Low-trust consumers are relatively insensitive to textual errors in judging reviewer credibility. However, high-trust consumers are less forgiving of typographical errors (which may signal carelessness) than orthographical errors (which may indicate cognitive challenges). Implications for future research are discussed.
Surge Capacity Deployment in Hospitals: Effectiveness of Response and Mitigation Strategies

Problem definition: Major hospitals frequently lack adequate space to accommodate emergency patients. Managers can take actions to create surge capacity, an immediate additional supply of medical s...
ON THE EDGE-BALANCED INDEX SETS OF PRODUCT GRAPHS

We characterize strongly edge regular product graphs and find the edge-balanced index sets of complete bipartite graphs without a perfect matching, the direct product Kn X K2. We also prove a lemma that is helpful to determine theedge-balanced index sets of regular graphs. DOI : http://dx.doi.org/10.22342/jims.0.0.20.71-78
Understanding the Drivers of Household Energy Spending: Micro Evidence for Latin America

The paper investigates the determinants of household energy spending and energy budget shares, with a focus on understanding their non-linear relationship with income, and the presence of economies of scale. The analysis is based on a unique, harmonized collection of official household surveys from 13 Latin American countries. This dataset allows distinguishing between expenditures on electricity, domestic gas, and fuel for private transportation, providing a comprehensive distributional view of the energy spending profile of the residential sector. The estimated empirical Engel curves behave similarly; however, the derived income elasticities show marked distinctions by fuel, and their actual values depend on the householdsâ€™ relative position over the income distribution. For electricity, the elasticity tends to increase in income but stabilize at the wealthiest segments. For gas and transport fuel, it decreases under different income paths. In this dataset, the examination returns income elasticities on the (0,1) interval, suggesting that energy commodities are necessity goods. However, the distribution of aggregate energy expenditure needs to be considered. Specifically, there is a great concentration among the richer groups, particularly for transport fuels, where the top quintile gathers more than half of the aggregate spending. The results also indicate economies of scale â€“â€“for electricity and domestic gasâ€“â€“ with respect to family-age composition, and to a lesser extent with respect to dwelling size. In the case of electricity, these economies are more pronounced for richer households. These results join the previous literature in emphasizing the relevance of accounting for household demographic and socioeconomic trends for energy management.
Searching for exoplanets using artificial intelligence

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
Magnetohydrodynamic simulation study of plasma jets and plasma-surface contact in coaxial plasma accelerators

Recent experiments by Loebner et al. [IEEE Trans. Plasma Sci. 44, 1534 (2016)] studied the effect of a hypervelocity jet emanating from a coaxial plasma accelerator incident on target surfaces in an effort to mimic the transient loading created during edge localized mode disruption events in fusion plasmas. In this paper, we present a magnetohydrodynamic (MHD) numerical model to simulate plasma jet formation and plasma-surface contact in this coaxial plasma accelerator experiment. The MHD system of equations is spatially discretized using a cell-centered finite volume formulation. The temporal discretization is performed using a fully implicit backward Euler scheme and the resultant stiff system of nonlinear equations is solved using the Newton method. The numerical model is employed to obtain some key insights into the physical processes responsible for the generation of extreme stagnation conditions on the target surfaces. Simulations of the plume (without the target plate) are performed to isolate and ...
Opportunistic Infections and Kaposi’s Sarcoma in Homosexual Men

In the summer of 1981, the Centers for Disease Control (CDC) alerted the medical world to an unexpected outbreak of Pneumocystis carinii pneumonia and Kaposi's sarcoma in young homosexual men who h...