cssi/SciSciGPT-SciSciNet huggingface dataset:
Reduced phase noise in an erbium frequency comb via intensity noise suppression

We present a coherent erbium fiber frequency comb that achieves low phase noise operation through the active suppression of amplitude fluctuations within the laser oscillator. The amplitude noise servo has a bandwidth of 550 kHz and is achieved by current actuation of the laser pump diode. This servo reduces the integrated phase noise of the carrier envelope offset frequency of the comb, fceo, due to the strong coupling of amplitude and phase noise in the laser oscillator. Additionally, we use a composite error signal that utilizes information from both the amplitude noise and the fceo error signal to actuate the pump diode current, which further increases the coherence of the comb. With this locking scheme, the integrated phase noise on fceo is measured to be 270 mrad from 10 Hz to 1.5 MHz, indicating 93% of the optical carrier power is in the coherent signal. A simultaneous phase lock to a narrow-linewidth continuous-wave laser is achieved by actuating on the cavity length, and shows an integrated phase noise of 44 mrad.
Too Stressed to Teach? Teaching Quality, Student Engagement, and IEP Outcomes:

Teacher stress and burnout have a detrimental effect on the stability of the teaching workforce. However, the possible consequences of teacher burnout on teaching quality and on student learning ou...
Consolidation treatment with lenalidomide following front-line or salvage chemoimmunotherapy in chronic lymphocytic leukemia.

The anti-tumoral activity of lenalidomide occurs via multiple mechanisms, including repair of chronic lymphocytic leukemia-induced immune defects.[1][1] We therefore hypothesized that it would be an effective and safe consolidation strategy in patients with chronic lymphocytic leukemia (CLL) who
The effect of high pressure on the electronic structure of solids

Publisher Summary This chapter discusses the experimental activities in laboratory on the electronic structure of solids at pressures to perhaps 600 kbar. Experiments in the range beyond 100 kbar encounter limitations as to the variety of measurements that is possible, as to the accuracy with which the pressure is known, and as regards hydrostaticity of the pressure. There are, however, important compensations. The volume decreases are large (20-50% in metals and frequently more in nonmetals); and one can observe second- and third-order effects beyond the direction and magnitude of the first-order pressure shift of a variable. Many qualitatively new events are observed-continuous and discontinuous transitions from nonmetal to metal, electronic transitions, and changes from ferromagnetic to nonferromagnetic states. Included in this discussion are optical absorption measurements to 160 kbar, phosphor emission and decay to 50 kbar, electrical resistance measurements to about 600 kbar, X-ray diffraction studies to 500 kbar, and Mossbauer measurements to 250 kbar.
Fake News Propagation and Detection: A Sequential Model

In the wake of the 2016 U.S. presidential election, social-media platforms are facing increasing pressure to combat the propagation of “fake news” (i.e., articles whose content is fabricated). Moti...
Loss Aversion and Anchoring in Commercial Real Estate Pricing: Empirical Evidence and Price Index Implications

Loss aversion behavior plays a major role in the pricing of commercial properties, and it varies both across the type of market participants and across the cycle. We find that sophisticated and more experienced investors are at least as loss averse as their counterparts and that loss aversion operated most strongly during the cycle peak in 2007. We also document a possible anchoring effect of the asking price in influencing buyer valuation and subsequent transaction price. We demonstrate the importance of behavioral phenomena in constructing hedonic price indices, and we find that the impact of loss aversion is attenuated at the aggregate market level. This suggests that the pricing and volume cycle during 2001–2009 was little affected by loss aversion.
Grain boundary phases in bcc metals

We report a computational discovery of novel grain boundary structures and multiple grain boundary phases in elemental body-centered cubic (bcc) metals represented by tungsten, tantalum and molybdenum. While grain boundary structures created by the γ-surface method as a union of two perfect half crystals have been studied extensively, it is known that the method has limitations and does not always predict the correct ground states. Herein, we use a newly developed computational tool, based on evolutionary algorithms, to perform a grand-canonical search of high-angle symmetric tilt and twist boundaries, and we find new ground states and multiple phases that cannot be described using the conventional structural unit model. We use molecular dynamics (MD) simulations to demonstrate that the new structures can coexist at finite temperature in a closed system, confirming that these are examples of different grain boundary phases. The new ground state is confirmed by first-principles calculations.
How Does Welfare from Load Shifting Electricity Policy Vary with Market Prices? Evidence from Bulk Storage and Electricity Generation

We model the electricity market to demonstrate that changes in the price of natural gas can cause the market and non-market impacts of bulk electricity storage to move in opposite directions. We provide evidence consistent with the model using a series of reduced form tests on data from 2005-2010. We then simulate installing bulk electricity storage on the US electric grid. We find that lower natural gas prices generally reduce the market gains and non-market costs of storage. However, direct non-market costs are still positive which means that there is no argument for subsidizing storage to mitigate pollution given the current mix of generating technologies; arguments in favor of bulk storage R&D subsidies ride on public good aspects of technology and dynamic investment incentives for intermittent renewables.
Selective Attention Gates the Interactive Crossmodal Coupling between Perceptual Systems

Summary Sensory cortical systems often activate in parallel, even when stimulation is experienced through a single sensory modality [1–3]. Co-activations may reflect the interactive coupling between information-linked cortical systems or merely parallel but independent sensory processing. We report causal evidence consistent with the hypothesis that human somatosensory cortex (S1), which co-activates with auditory cortex during the processing of vibrations and textures [4–9], interactively couples to cortical systems that support auditory perception. In a series of behavioral experiments, we used transcranial magnetic stimulation (TMS) to probe interactions between the somatosensory and auditory perceptual systems as we manipulated attention state. Acute TMS over S1 impairs auditory frequency perception when subjects simultaneously attend to auditory and tactile frequency, but not when attention is directed to audition alone. Auditory frequency perception is unaffected by TMS over visual cortex, thus confirming the privileged interactions between the somatosensory and auditory systems in temporal frequency processing [10–13]. Our results provide a key demonstration that selective attention can modulate the functional properties of cortical systems thought to support specific sensory modalities. The gating of crossmodal coupling by selective attention may critically support multisensory interactions and feature-specific perception.
The Light and the Heat: Productivity Co-Benefits of Energy-Saving Technology

We study the adoption of energy-efficient LED lighting in garment factories around Bangalore, India. Combining daily production line–level data with weather data, we estimate a negative, nonlinear ...
Photoemission signature of excitons

Excitons, the particle-hole bound states, composed of localized electron-hole states in semiconducting systems, are crucial to explaining the optical spectrum. Spectroscopic measurements can contain signatures of these two-particle bound states and can be particularly useful in determining the characteristics of these excitons. We formulate an expression for evaluating the angle-resolved photoemission spectrum arising from the ionization of excitons given their steady-state distribution in a semiconductor. We show that the spectrum contains information about the direct/indirect band gap nature of the semiconductor and is located below the conduction band minimum displaced by the exciton binding energy. The dispersive features of the spectrum contains remnants of the valence band while additional interesting features arise from different exciton distributions. Our results indicate that for most exciton probability distributions, the energy-integrated photoemission spectrum provides an estimate of the exciton Bohr radius.
Vibration Absorbers for a Mistuned Bladed Disk

Mistuning refers to variations in modal properties of blades due to manufacturing tolerances. This can result in the amplification of a blade vibratory amplitude. This paper deals with the design of vibration absorbers for a mistuned bladed disk. First, the basic theory is established for undamped vibration absorbers using a single mode model for each blade. Then, it is extended to include a multiple mode model of each blade and disk dynamics. The impact of mistuning on the bladed disk vibration is examined in the presence of undamped absorbers via Monte Carlo simulations. It is found that vibration absorbers can be an effective method to counter the detrimental effects of mistuning.Copyright © 2016 by ASME
The middle productivity trap: Dynamics of productivity dispersion

Abstract Using a worldwide firm-level panel dataset I document a “U-shaped” relationship between productivity growth and baseline levels within each country and industry. That is, fast productivity growth is concentrated at both ends of the productivity distribution. This result serves as a potential explanation to two stylized facts documented in the economic literature: the rising productivity dispersion within narrowly defined sectors, and the increasing market share of few yet highly productive firms. I further provide evidence suggesting that such pattern is driven by knowledge-intensive industries, for which cross-firm diffusion of knowledge is presumably more costly.
Statistical Witness Indistinguishability (and more) in Two Messages

Two-message witness indistinguishable protocols were first constructed by Dwork and Naor (FOCS 2000). They have since proven extremely useful in the design of several cryptographic primitives. However, so far no two-message arguments for NP provided statistical privacy against malicious verifiers. In this paper, we construct the first: \(\circ \) Two-message statistical witness indistinguishable (SWI) arguments for NP. \(\circ \) Two-message statistical zero-knowledge arguments for NP with super-polynomial simulation (Statistical SPS-ZK). \(\circ \) Two-message statistical distributional weak zero-knowledge (SwZK) arguments for NP, where the simulator is a probabilistic polynomial time machine with oracle access to the distinguisher, and the instance is sampled by the prover in the second round.
Environmental Benefits of Internet-Enabled C2C Closed-Loop Supply Chains: A Quasi-Experimental Study of Craigslist

Recently, online matching platforms (e.g., Craigslist, FreeCycle, Gumtree) have enabled consumers to directly connect with each other to buy/sell used consumer goods (electronics, furniture, packaging, etc.) that would have otherwise ended up in the waste stream. Such matching platforms can facilitate the creation of consumer-to-consumer (C2C) closed-loop supply chains (CLSCs) for used goods, which can enhance product reuse and limit reliance on recycling and disposal alternatives. Yet, the true environmental benefits of these internet-enabled C2C CLSCs remain to be ascertained. In this study, I use a quasi-experimental setup to examine how Craigslist’s entry into various U.S. geographic markets impacts a key environmental outcome: municipal solid waste (MSW). I assemble a data set from various disparate sources to test my hypothesis. I find that, on average, Craigslist’s entry into a geographic market results in a 2%–6% annual reduction in MSW per capita generated. I conduct a variety of robustness check...
Task-Based Effectiveness of Basic Visualizations

Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types—Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart—across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.
The 2014-16 oil price collapse in retrospect : sources and implications

With the benefit of hindsight, this paper provides a fresh and comprehensive look at the causes of the 2014-16 collapse in oil prices and its impact on the global economy. It disentangles the contribution of supply and demand factors, assesses the impact on activity in oil exporters and oil importers, and reviews policy responses in these countries. The main conclusions are: (i) the decline in oil prices was predominantly triggered by supply factors, particularly rapid efficiency gains in U.S. shale oil production, but softening demand prospects played a substantial role as well; (ii) the short-term benefits of falling oil prices for the global economy were muted by economic rebalancing in China, a low responsiveness of activity in other oil-importing emerging markets, and a sharp slowdown in U.S. investment as energy sector activity declined and a the U.S. dollar strengthened; (iii) oil exporters with flexible exchange rates and relatively large fiscal buffers fared better than others, but most oil-exporting economies still face significant policy challenges as their medium-term prospects for growth and fiscal revenues have deteriorated; (iv) fundamental changes in the oil market make a return to the oil price levels of the early 2010s unlikely, pointing to the need for accelerated reforms, particularly among oil exporters.
What Is the Best Pain Control After Major Hepatopancreatobiliary Surgery

Currently, EA is supported by high-level evidence, specifically in liver surgery, to be the most effective analgesic modality for pain control after HPB surgery. Additional high-level evidence for superior analgesic modalities after pancreatectomies is required. Subsequent randomized controlled trials are required to elucidate the effectiveness and safety of new strategies such as a TAP block compared to EA for both hepatectomies and pancreatectomies in the setting of ER. Beyond adequate pain control and total opiate consumption, PRO tools and the ability to RIOT in cancer patients should be secondary outcome measure in all future studies.
Function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions

Abstract A zonal function (ZF) network on the q dimensional sphere S q is a network of the form x ↦ ∑ k = 1 n a k ϕ ( x ⋅ x k ) where ϕ : [ − 1 , 1 ] → R is the activation function, x k ∈ S q are the centers, and a k ∈ R . While the approximation properties of such networks are well studied in the context of positive definite activation functions, recent interest in deep and shallow networks motivate the study of activation functions of the form ϕ ( t ) = | t | , which are not positive definite. In this paper, we define an appropriate smoothness class and establish approximation properties of such networks for functions in this class. The centers can be chosen independently of the target function, and the coefficients are linear combinations of the training data. The constructions preserve rotational symmetries.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.
Boundedness and invariant metrics for diffeomorphism cocycles over hyperbolic systems

Let $$\mathcal {A}$$ be a Holder continuous cocycle over a hyperbolic dynamical system with values in the group of diffeomorphisms of a compact manifold $${\mathcal {M}}$$ . We consider the periodic data of $$\mathcal {A}$$ , i.e., the set of its return values along the periodic orbits in the base. We show that if the periodic data of $$\mathcal {A}$$ is bounded in $$\text{ Diff }^{\,q}({\mathcal {M}})$$ , $$q>1$$ , then the set of values of the cocycle is bounded in $$\text{ Diff }^{\,r}({\mathcal {M}})$$ for each $$r<q$$ . Moreover, such a cocycle is isometric with respect to a Holder continuous family of Riemannian metrics on $${\mathcal {M}}$$ .
Normal Neutrophil Function in Human Renal Allograft Recipients Receiving Minnesota Anti-Lymphoblast Globulin (MAG)

Equine antihuman lymphoblast globulin (MAG) used in combination with other immunosuppressive agents did not impair chemotaxis, phagocytosis, nitroblue tetrazolium (NBT) reduction or halogenation by peripheral blood neutrophils (PMN) from seven human recipients of cadaver renal allografts. MAG was given to the allograft recipients following transplantation, and the dosage was determined each day by quantitation of peripheral blood T and B lymphocytes. No deposition of immunoglobulin or complement on the recipient’s PMN was detected by indirect immunofluorescence. Chemotaxis of PMN was determined in blind-well chambers, and phagocytosis of opsonized zymosan was quantitated by light microscopy. Reduction of NBT was quantitated by spectrophotometric measurement of solubilized formazan, and halogenation was measured by quantitating the incorporation of 125I into PMN that had phagocytosed opsonized zymosan. Chemotaxis, phagocytosis, NBT reduction, and halogenation by neutrophils from patients receiving MAG, prednisone and azathioprine were not impaired.
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus. Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations. We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness. We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.
Cohomology of Unipotent Group Schemes

We verify that universal classes in the cohomology of GLN determine explicit cohomology classes of Frobenius kernels G(r) of various linear algebraic groups G. We consider the relationship of $\varprojlim _{r} H^{*}(U_{(r)},k)$ to the rational cohomology H*(U,k) of many unipotent algebraic groups U. The second half of this paper investigates in detail the cohomology of Frobenius kernels (U3)(r) of the Heisenberg group U3 ? GL3.
Earnings Announcement Disclosures and Changes in Analysts’ Information

This study examines how financial disclosures with earnings announcements affect sell-side analysts’ information about future earnings, focusing on disclosures of financial statements and management earnings forecasts. We find that disclosures of balance sheets and segment data are associated with an increase in the degree to which analysts’ forecasts of upcoming quarterly earnings are based on private information. Further analyses show that balance sheet disclosures are associated with an increase in the precision of both analysts’ common and private information, segment disclosures are associated with an increase in analysts’ private information, and management earnings forecast disclosures are associated with an increase in analysts’ common information. These results are consistent with analysts processing balance sheet and segment disclosures into new private information regarding near-term earnings. Additional analysis of conference calls shows that balance sheet, segment, and management earnings forecast disclosures are all associated with more discussion related to these items in the questions-and-answers section of conference calls, consistent with analysts playing an information interpretation role with respect to these disclosures. This article is protected by copyright. All rights reserved.
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, and higher-order derivatives. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT.
Algebraic Optimization of Binary Spatially Coupled Measurement Matrices for Interval Passing

We consider binary spatially coupled (SC) low density measurement matrices for low complexity reconstruction of sparse signals via the interval passing algorithm (IPA). The IPA is known to fail due to the presence of harmful sub-structures in the Tanner graph of a binary sparse measurement matrix, so called termatiko sets. In this work we construct array-based (AB) SC sparse measurement matrices via algebraic lifts of graphs, such that the number of termatiko sets in the Tanner graph is minimized. To this end, we show for the column-weight-three case that the most critical termatiko sets can be removed by eliminating all length-12 cycles associated with the Tanner graph, via algebraic lifting. As a consequence, IPA-based reconstruction with SC measurement matrices is able to provide an almost error free reconstruction for significantly denser signal vectors compared to uncoupled AB LDPC measurement matrices.
Network Coding for Critical Infrastructure Networks

The applications in the critical infrastructure systems pose simultaneous resilience and performance requirements to the underlying computer network. To meet such requirements, the networks that use the store-and-forward paradigm poses stringent conditions on the redundancy in the network topology and results in problems that becoming computationally challenging to solve at scale. However, with the advent of programmable data-planes, it is now possible to use linear network coding (NC) at the intermediate network nodes to meet resilience requirements of the applications. To that end, we propose an architecture that realizes linear NC in programmable networks by decomposing the linear NC functions into the atomic coding primitives. We designed and implemented the primitives using the features offered by the P4 ecosystem. Using an empirical evaluation, we show that the theoretical gains promised by linear network coding can be realized with a per-packet processing cost.
ScratchVR: low-cost, calibration-free sensing for tactile input on mobile virtual reality enclosures

We extend the interaction space of low-cost mobile virtual reality (VR) by introducing bidirectional scrolling and discrete selection using magnetic sensing. Our design uses the original Google Cardboard v1 input components, modifying only the cardboard mounted on the side. Users slide the magnetized washer around a circular track on the outer layer, which drags a magnet on the inner layer across asymmetric patterned ridges. The phone's magnetometer detects the position of the magnet as it moves around the track and slots into each ridge, emulating a click wheel. The phone's accelerometer is used to recognize center button taps. We compare our system against the current best practice (gaze) with 12 participants across four VR navigation and selection tasks. Finally, we demonstrate our system robustly handles continuous input, despite some minor deterioration of the cardboard, using a motorized rig over an 8-hour period.
RADIANS: A Multidisciplinary Central Nervous System Clinic Model for Radiation Oncology and Neurosurgery Practice

Background Radiation therapy for central nervous system disease commonly involves collaboration between Radiation Oncology and Neurosurgery. We describe our early experience with a multidisciplinary clinic model. Methods In 2016, the novel RADIANS (RADIation oncology And NeuroSurgery) clinic model was initiated at a community hospital. Disease and treatment demographics were collected and analyzed. Patient satisfaction was assessed via a blinded survey questionnaire. Results Forty-two patients have been seen since the inception of RADIANS. The median age was 65; and the median patient distance from RADIANS was 42.7 miles (mean = 62.6; range = 0.7–285). Half of the patients traveled >50 miles to receive care, and >80% were seen for central nervous system metastases. Of the patients receiving radiation, 75% received stereotactic radiosurgery/stereotactic body radiation therapy. The mean overall satisfaction from 0 (not satisfied) to 5 (very satisfied) was 4.8. Conclusions The RADIANS clinic model has proved viable and well-liked by patients in a community setting, with the majority of radiation therapy administered being stereotactic radiosurgery/stereotactic body radiation therapy rather than conventional fractionation.
Potential benefit of amorphization in the retention of gaseous species in irradiated pyrochlores

Abstract Understanding the structure-property relationship for materials destined for irradiation extremes is a key step in developing materials with reliable, long-term performance. One crucial relationship is the ability of a material to withstand or accommodate amorphization, as this dictates its potential use as a nuclear waste form. Pyrochlores are one such class of materials for consideration as waste forms and there has been significant work examining how both the crystal structure and chemistry impacts amorphization resistance, leading to the important conclusion that the amorphization resistance of pyrochlores (A2B2O7) is very sensitive to the nature of the B cations. For example, pyrochlores with B Ti amorphize much more readily than B Zr compounds. However, there are still questions regarding how these types of materials respond to prolonged or high-dose irradiation conditions. In this work, Gd2Ti2O7 (GTO) and Gd2Zr2O7 (GZO) pyrochlores were implanted with 400 keV Kr++ ions at room temperature to calculated peak damages of 119 and 135 displacements per atom (dpa), respectively. As expected, GTO amorphized completely under irradiation. However, discrete bubbles of Kr coalesced within the amorphous matrix without micro-cracking or spallation. In contrast, GZO transforms to a disordered fluorite structure under irradiation with no indications of localized amorphization. But, the accumulation of Kr within the host material leads to sub-grain structures, extended defects, and the development of micro-cracks. Thus, while GTO readily amorphizes even under low dose irradiations, the resistance of the amorphous GTO matrix to micro cracking and gas release, even in the presence of large bubble formation, suggests an enhanced propensity to retain gaseous species. Consideration of long-term dose accumulation effects in nuclear waste forms would suggest reconsideration of amorphization processes in pyrochlores and related materials as a potential beneficial effect for immobilization and long-term storage of actinide materials.
Trust in scientists and rates of noncompliance with a fisheries rule in the Brazilian Pantanal.

Natural resource rules exist to control resources and the people that interact with them. These rules often fail because people do not comply with them. Decisions to comply with natural resource rules often are based on attitudes about legitimacy of rules and the perceived risks of breaking rules. Trust in agencies promulgating rules in part may determine perceptions of legitimacy of the rule, and in turn depends on individuals’ trust in different agency actors. The purpose of this research is to explore the relationship between fishing rule noncompliance and trust in scientists, a key group within management agencies. We interviewed 41 individuals in one rural fishing community in the Brazilian Pantanal from April to August, 2016, to assess (1) noncompliance rates, (2) noncompliance-related attitudes, and (3) the relationship between trust in scientists and noncompliance decisions in the region. We found that among study participants, noncompliance was common and overt. Trust in scientists performing research in the region was the best predictor of noncompliance rate with a fishing rule (nonparametric rank correlation ρ = -0.717; Probit model pseudo-R2 = 0.241). Baseline data from this research may help inform future interventions to minimize IUU fishing and protect the Pantanal fishery. Although our results are specific to one community in the Pantanal, trust in scientists is potentially an important factor for compliance decisions in similar situations around the world. These results build not only on compliance theory but also speak to the important role that many scientists play in rural areas where they conduct their research.
Wage discrimination in the NBA: Evidence using free agent signings

This article takes a step toward understanding potential causes of wage discrimination in an economy highly dependent on the service industry. Traditionally categorized within the service industry, professional sport leagues such as the National Basketball Association (NBA) provides a unique setting to examine the potential impact of race on salary. We analyze free agency contract signings, which allow us to better capture the determinants of players' wages, from 2011 to 2017 to investigate the prominence of wage discrimination in the NBA. Using weighted linear regression models and the Oaxaca–Blinder decomposition, we find that Black athletes are paid significantly less than their counterparts. In addition, we also identify the presence of consumer discrimination after controlling the exposure of a player to the audience, which is observed through the interaction term between the share of MSA population which is White and an identifier for whether a player is Black.
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.
Share, but unequally: a plausible mechanism for emergence and maintenance of intratumour heterogeneity.

Intratumour heterogeneity (ITH), referring to the coexistence of different cell subpopulations in a single tumour, has been a major puzzle in cancer research for almost half a century. The lack of ...
Treatment of girls and boys with McCune-Albright syndrome with precocious puberty - Update 2017

The most common endocrinopathy associated with McCune-Albright Syndrome (MAS) is peripheral precocious puberty (PP) which occurs far more often in girls than in boys. We will discuss the latest advancements in the treatment of precocious puberty in MAS that have been achieved during the past 10 years. However, due to the rarity of the condition and the heterogeneity of the disease, research in this field is limited particularly in regards to treatment in boys. In girls, a period of watchful waiting is recommended prior to initiating therapy due to extreme variability in the clinical course. This article will review in detail current pharmacologic treatment in girls, which typically consists of either inhibiting estrogen production or blocking estrogen action at the level of the end-organ. The two treatments with the most evidence at this time are Tamoxifen (which is an estrogen receptor modulator) and Letrozole (which is a 3rd generation aromatase inhibitor). This article will also review the current treatment strategies in boys which typically include using an androgen receptor blocker and an aromatase inhibitor. Due to the rarity of the condition, large multicenter collaborative studies are needed to further investigate efficacy and safety with the goal of establishing the gold standard for treatment of PP in children with MAS.
Viking lander tracking contributions to mars mapping

The major recent advances in planetary mapping have been accomplished through use of photography from orbiting satellites, as is the case for Mars with Mariner and Viking photographs. The requirement for greater precision demands that inputs to the photogrammatic process be more precisely defined. This paper describes how analyses of Doppler and ranging data from the Viking landers are contributing to more precise mapping of Mars in several specific areas.
Impact of Correlation Between Wind Speed and Turbine Availability on Wind Farm Reliability

This paper proposes a new method to evaluate the reliability of a wind farm considering the correlation between wind turbine reliability and wind speed. With increasing integration of wind generation into the grid, the reliability and stability of the grid are increasingly impacted. Although there are obvious benefits from wind generation, the stochastic nature of wind speed causes several operational challenges. Recent research has shown that wind speeds also impact the failure rates of the turbines, thereby compounding the effect of wind speed variation. The objective of this paper is to evaluate the impact of wind speed on the reliability of a wind farm considering the correlation between wind speed and wind turbine failure rate. The method proposed in this paper is implemented using discrete convolution. The effectiveness of the proposed method is proved by comparing reliability indexes of the modified IEEE RTS-79 system with and without considering the impacts of the negative correlation between wind turbine reliability and wind speed.
Instance-Specific Algorithm Configuration

This book presents a modular and expandable technique in the rapidly emerging research area of automatic configuration and selection of the best algorithm for the instance at hand. The author presents the basic model behind ISAC and then details a number of modifications and practical applications. In particular, he addresses automated feature generation, offline algorithm configuration for portfolio generation, algorithm selection, adaptive solvers, online tuning, and parallelization. The author's related thesis was honorably mentioned (runner-up) for the ACP Dissertation Award in 2014, and this book includes some expanded sections and notes on recent developments. Additionally, the techniques described in this book have beensuccessfullyapplied to a number of solvers competing in the SATand MaxSAT International Competitions, winning a total of 18 gold medals between 2011 and 2014. The book will be of interest to researchers and practitioners in artificial intelligence, in particular in the area of machine learning and constraint programming.
Intraperitoneal delivery of a novel drug-like compound improves disease severity in severe and intermediate mouse models of Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disorder that causes progressive muscle weakness and is the leading genetic cause of infant mortality worldwide. SMA is caused by the loss of survival motor neuron 1 (SMN1). In humans, a nearly identical copy gene is present, called SMN2. Although SMN2 maintains the same coding sequence, this gene cannot compensate for the loss of SMN1 because of a single silent nucleotide difference in SMN2 exon 7. SMN2 primarily produces an alternatively spliced isoform lacking exon 7, which is critical for protein function. SMN2 is an important disease modifier that makes for an excellent target for therapeutic intervention because all SMA patients retain SMN2. Therefore, compounds and small molecules that can increase SMN2 exon 7 inclusion, transcription and SMN protein stability have great potential for SMA therapeutics. Previously, we performed a high throughput screen and established a class of compounds that increase SMN protein in various cellular contexts. In this study, a novel compound was identified that increased SMN protein levels in vivo and ameliorated the disease phenotype in severe and intermediate mouse models of SMA.
Impact of the Stringency of Attendance Policies on Class Attendance/Participation and Course Grades.

The purpose of this preliminary study was to investigate the impact of three diverse attendance and participation policies in face-to-face and online courses and the effect on students’ final grades in each course. We examined nine different undergraduate courses taught between Fall 2010 and Spring 2015. The results suggest that a more stringent attendance policy significantly impacts student attendance, absences were negatively correlated with course grades, and that course delivery methods were not predictive of either attendance/participation or course grades. Additional research is needed to determine what other factors might influence attendance and participation and correlation to course grades.
IT Impact on Innovation at the Individual and Group Level – A Literature Review

Information technology (IT) is broadly recognized as an important element that supports innovation within organizations, however there has been relatively little integration of research in Informat...
Machine Learning for Classification of Protein Helix Capping Motifs

The biological function of a protein stems from its 3-dimensional structure, which is thermodynamically determined by the energetics of interatomic forces between its amino acid building blocks (the order of amino acids, known as the sequence, defines a protein). Given the costs (time, money, human resources) of determining protein structures via experimental means like X-ray crystallography, can we better describe and compare protein 3D structures in a robust and efficient manner, so as to gain meaningful biological insights? We begin by considering a relatively simple problem, limiting ourselves to just protein secondary structural elements. Historically, many computational methods have been devised to classify amino acid residues in a protein chain into one of several discrete “secondary structures”, of which the most well-characterized are the geometrically regular $\mathbf{a}$ -helix and $\boldsymbol{\beta}$ -sheet; irregular structural patterns, such as ‘turns’ and ‘loops’, are less understood. Here, we present a study of Deep Learning techniques to classify the loop-like end cap structures which delimit a-helices. Previous work used highly empirical and heuristic methods to manually classify helix capping motifs. Instead, we use structural data directly—including (i) backbone torsion angles computed from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical properties), and (iii) helix cap classification data (from CAPS-DB)—as the ground truth to train a bidirectional long short–term memory (BiLSTM) model to classify helix cap residues. We tried different network architectures and scanned hyperparameters in order to train and assess several models; we also trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we achieved 85% class-balanced accuracy with a deep BiLSTM model.
A mutation in the endonuclease domain of mouse MLH3 reveals novel roles for MutLγ during crossover formation in meiotic prophase I

During meiotic prophase I, double-strand breaks (DSBs) initiate homologous recombination leading to non-crossovers (NCOs) and crossovers (COs). In mouse, 10% of DSBs are designated to become COs, primarily through a pathway dependent on the MLH1-MLH3 heterodimer (MutLγ). Mlh3 contains an endonuclease domain that is critical for resolving COs in yeast. We generated a mouse (Mlh3DN/DN) harboring a mutation within this conserved domain that is predicted to generate a protein that is catalytically inert. Mlh3DN/DN males, like fully null Mlh3-/- males, have no spermatozoa and are infertile, yet spermatocytes have grossly normal DSBs and synapsis events in early prophase I. Unlike Mlh3-/- males, mutation of the endonuclease domain within MLH3 permits normal loading and frequency of MutLγ in pachynema. However, key DSB repair factors (RAD51) and mediators of CO pathway choice (BLM helicase) persist into pachynema in Mlh3DN/DN males, indicating a temporal delay in repair events and revealing a mechanism by which alternative DSB repair pathways may be selected. While Mlh3DN/DN spermatocytes retain only 22% of wildtype chiasmata counts, this frequency is greater than observed in Mlh3-/- males (10%), suggesting that the allele may permit partial endonuclease activity, or that other pathways can generate COs from these MutLγ-defined repair intermediates in Mlh3DN/DN males. Double mutant mice homozygous for the Mlh3DN/DN and Mus81-/- mutations show losses in chiasmata close to those observed in Mlh3-/- males, indicating that the MUS81-EME1-regulated crossover pathway can only partially account for the increased residual chiasmata in Mlh3DN/DN spermatocytes. Our data demonstrate that mouse spermatocytes bearing the MLH1-MLH3DN/DN complex display the proper loading of factors essential for CO resolution (MutSγ, CDK2, HEI10, MutLγ). Despite these functions, mice bearing the Mlh3DN/DN allele show defects in the repair of meiotic recombination intermediates and a loss of most chiasmata.
1,4-Addition of an aryllithium reagent to diethyl ketomalonate. Scalable synthesis of ethyl 1-(hydroxymethyl)-1,3-dihydroisobenzofuran-1-carboxylate

While optimizing the synthesis of pharmaceutical building block 3 [ethyl 1-(hydroxymethyl)-1,3-dihydroisobenzofuran-1-carboxylate], we encountered an unusual addition of an aryllithium reagent to the ketone oxygen atom of diethyl ketomalonate. Compound 3 was ultimately prepared on a large scale by a two-step sequence involving (1) annulation of a functionalized Grignard reagent with diethyl ketomalonate and (2) selective mono-reduction of a geminal diester using lithium tri-tert-butoxyaluminum hydride.
Facile synthesis of α-monofluoromethyl alcohols: Nucleophilic monofluoromethylation of aldehydes using TMSCF(SO2Ph)2

Abstract α-Fluoromethyl phenyl sulfone derivatives have been extensively employed in various reactions as versatile fluoromethylating reagents. While nucleophilic monofluoromethylations of aldehydes have been achieved using fluoromethyl phenyl sulfone or fluorobis(sulfonyl)methanes, a facile protocol under mild reaction conditions remains an ardently sought goal. We now report a feasible synthetic approach toward β-monofluorinated alcohols using α-trimethylsilyl-α-fluorobis(phenylsulfonyl)methane [TMSCF(SO2Ph)2, TFBSM] as a novel monofluoromethylating reagent. Initiated by a catalytic amount of fluoride, the reagent can be readily added to a variety of aldehydes providing the desired products in high yields. Computational and kinetic studies have revealed the exceptional lability of the Si–C bond in TFBSM compared with other fluoromethylsilane counterparts.
Tibanna: software for scalable execution of portable pipelines on the cloud.

Summary We introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network. Availability and implementation Source code is available on GitHub at https://github.com/4dn-dcic/tibanna. Supplementary information Supplementary data are available at Bioinformatics online.
A new organic transformation by introducing crotyl/allyltrifluoroborates in cross-coupling reaction with aroyl chlorides

Abstract Microwave irradiated PdCl2(dtbpf) catalyzed direct cross-coupling reaction of aroyl chlorides with potassium crotyl/allyltrifluoroborates has been developed. Regioselectivity of the cross-coupling product is varied from crotyltrifluoroborate to allyltrifluoroborate.