cssi/SciSciGPT-SciSciNet huggingface dataset:
Polo-like kinase 1 (Plk1) overexpression enhances ionizing radiation-induced cancer formation in mice

Polo-like kinase 1 (Plk1), a serine/threonine protein kinase normally expressed in mitosis, is frequently up-regulated in multiple types of human tumors regardless of the cell cycle stage. However, the causal relationship between Plk1 up-regulation and tumorigenesis is incompletely investigated. To this end, using a conditional expression system, here we generated Plk1 transgenic mouse lines to examine the role of Plk1 in tumorigenesis. Plk1 overexpression in mouse embryonic fibroblasts prepared from the transgenic mice led to aberrant mitosis followed by aneuploidy and apoptosis. Surprisingly, Plk1 overexpression had no apparent phenotypes in the mice. Given that no malignant tumor formation was observed even after a long period of Plk1 overexpression, we reasoned that additional factors are required for tumorigenesis in Plk1-overexpressing mice. Because Plk1 can directly participate in the regulation of the DNA damage response (DDR) pathway, we challenged Plk1-overexpressing mice with ionizing radiation (IR) and found that Plk1-overexpressing mice are much more sensitive to IR than their wild-type littermates. Analysis of tumor development in the Plk1-overexpressing mice indicated a marked decrease in the time required for tumor emergence after IR. At the molecular level, Plk1 overexpression led to reduced phosphorylation of the serine/threonine kinases ATM and Chk2 and of histone H2AX after IR treatment both in vivo and in vitro. Furthermore, RNA-Seq analysis suggested that Plk1 elevation decreases the expression of several DDR genes. We conclude that Plk1 overexpression may contribute to tumor formation by both inducing chromosomal instability and suppressing the DDR pathway.
Diffraction imaging by prestack reverse‐time migration in the dip‐angle domain

Prestack image volumes may be decomposed into specular and non-specular parts by filters defined in the dip-angle domain. For space-shift extended image volumes, the dip-angle decomposition is derived via local Radon transform in depth and midpoint coordinates, followed by an averaging over space-shifts. We propose to employ prestack space-shift extended reverse-time migration and dip-angle decomposition for imaging small-scale structural elements, considered as seismic diffractors, in models with arbitrary complexity. A suitable design of a specularity filter in the dip-angle domain rejects the dominant reflectors and enhances diffractors and other non-specular image content. The filter exploits a clear discrimination in dip between specular reflections and diffractions. The former are stationary at the specular dip, whereas the latter are non-stationary without a preferred dip direction. While the filtered image volume features other than the diffractor images (for example, noise and truncation artifacts are also present), synthetic and field data examples suggest that diffractors tend to dominate, and are readily recognizable. Averaging over space-shifts in the filter construction makes the reflectors rejection robust against migration velocity errors. Another consequence of the space-shift extension and its angle-domain transforms is the possibility of exploring the image in a multiple set of common-image gathers. The filtered diffractions may be analyzed simultaneously in space-shift, scattering-angle, and dip-angle image-gathers, by means of a single migration job. The deliverables of our method obviously enrich the processed material on the interpreter's desk. We expect them to further supplement our understanding of the earth's interior. This article is protected by copyright. All rights reserved
Consumer Return Policies in Omnichannel Operations

We study the pricing and return policy decisions of an omnichannel retailer serving customers who differ in how they realize their uncertain valuation for a product—by inspecting in store before pu...
Labor Supply and the Value of Non-work Time: Experimental Estimates from the Field

We estimate the marginal value of non-work time (MVT) using a field experiment. We offered job applicants randomized wage-hour bundles. Choices over these bundles yield estimates of the MVT as a function of hours, tracing out a labor supply relationship. The substitution effect is positive. Individual labor supply is highly elastic at low hours and more inelastic at higher hours. For unemployed applicants, our preferred estimate of the average opportunity cost of a full-time job due to lost leisure and household production is 60 percent of after-tax marginal product, and 72 percent when including fixed costs of employment and child-care costs.
Chapter 21 – Finite Element Analysis Using ABAQUS∗

This chapter presents the solution of finite element problems using the commercial software ABAQUS. This software is based on Windows and Linux platforms and was developed and marketed by Advanced Franco-USA software from SIMULA, owned by Dassault Systems. The solution of a general problem by ABAQUS involves three stages - using ABAQUS Preprocessor, ABAQUS Solver and ABAQUS Post processor. The various steps involved in creating the input file, executing the program and output (related to the elements and the nodes), including graphical display, are illustrated using several examples. The example problems solved using ABAQUS in this chapter include – A fixed-fixed beam subjected to a concentrated load, a ten-bar planar truss under loads, a four-bar space truss subjected to loads at the free end, a twenty five - bar space truss under loads, a rectangular plate with fixed and simply supported on opposite edges and subjected to a concentrated transverse load at the center, and a L – shaped plate subjected to different temperatures on different edges for temperature analysis.
Fingers in a Hele–Shaw Cell with surface tension

McLean and Saffman’s model for the fingering in a Hele–Shaw cell is solved numerically. The results suggests that a countably infinite number of solutions exist for nonzero surface tension. This set of solutions contains the solution previously obtained by McLean and Saffman.
Maximum Entropy Low-Rank Matrix Recovery

We propose a novel, information-theoretic method, called MaxEnt , for efficient data acquisition for low-rank matrix recovery. This proposed method has important applications to a wide range of problems, including image processing and text document indexing. Fundamental to our design approach is the so-called maximum entropy principle, which states that the measurement masks that maximize the entropy of observations, also maximize the information gain on the unknown matrix $\mathbf {X}$ . Coupled with a low-rank stochastic model for $\mathbf {X}$ , such a principle 1) reveals novel connections between information-theoretic sampling and subspace packings, and 2) yields efficient mask construction algorithms for matrix recovery, which significantly outperform random measurements. We illustrate the effectiveness of MaxEnt in simulation experiments, and demonstrate its usefulness in two real-world applications on image recovery and text document indexing.
Impact of positive surgical margins on overall survival after partial nephrectomy-A matched comparison based on the National Cancer Database.

Abstract Introduction The impact of positive surgical margins (PSM) in partial nephrectomy (PN) has been a controversy. Previous studies on the relationship between PSM and overall survival (OS) were either underpowered or had highly dissimilar groups. We used the National Cancer Database with propensity score matching to determine the association between PSM and OS after PN. Materials and methods We identified patients with T1/T2 N0M0 renal cancer treated with PN between 2004 and 2009, and divided them into 2 groups based on their margin status. We used propensity score matching to ensure similarities in age, comorbidity score (CCI), tumor size, histology, and grade between groups. Covariates were compared by χ2 test. Cox multiple regression was used to estimate the hazard ratios (HR) for all-cause mortality. OS between matched groups were compared by log-rank, Breslow and Tarone-Ware tests. Results After excluding those with missing data on margin or survival status, 20,762 patients were eligible for matching. Each matched group had 1,265 patients, similar in age, sex, race, CCI, tumor size, histology, and grade. There were 386 recorded all-cause mortalities over a median follow-up duration of 72.6 months. Cox multiple regression showed a higher risk of all-cause mortality among cases with PSM (HR: 1.393, P = 0.001). Old age, high CCI, and large tumors had higher risks, while papillary and chromophore histologic subtypes had lower risks. PSM was associated with significantly worse OS by log-rank, Breslow, and Tarone-Ware tests. Conclusion PSM is associated with significantly worse OS after PN.
A SPR aptasensor for detection of avian influenza virus H5N1.

Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable affinity for target viruses and better thermal stability than monoclonal antibodies. The objective of this research was to use a DNA-aptamer as the specific recognition element in a portable Surface Plasmon Resonance (SPR) biosensor for rapid detection of AIV H5N1 in poultry swab samples. A SPR biosensor was fabricated using selected aptamers that were biotinylated and then immobilized on the sensor gold surface coated with streptavidin via streptavidin-biotin binding. The immobilized aptamers captured AIV H5N1 in a sample solution, which caused an increase in the refraction index (RI). After optimizing the streptavidin and aptamer parameters, the results showed that the RI value was linearly related (R2 = 0.99) to the concentration of AIV in the range of 0.128 to 1.28 HAU. Negligible signal ( < 4% of H5N1) was observed from six non-target AIV subtypes. The AIV H5N1 in poultry swab samples with concentrations of 0.128 to 12.8 HAU could be detected using this aptasensor in 1.5 h.
An evaluation of a research experience for teachers in nanotechnology

This study involves the evaluation of the second implementation of a Research Experiences for Teacher Advancement In Nanotechnology (RETAIN) program offered at Indiana University-Purdue University Indianapolis (IUPUI). RETAIN represents a professional development model for providing high school teachers with laboratory research experiences in nanoscience and related content areas. In this intensive summer program, teachers spend six weeks conducting nanotechnology-related research in an IUPUI lab. As part of the RETAIN program, teachers complete six credit hours of coursework, wherein they translate their research experiences into the design of classroom modules. Teachers are expected to then implement their modules within their own classrooms during the subsequent academic year. This evaluation focuses on teachers' experiences in IUPUI labs during the summer of 2016, along with three teachers' implementation of nanotechnology labs within their courses during the 2016–2017 school year. To evaluate RETAIN, we explored teacher satisfaction, changes in teachers' content knowledge and nanotechnology perceptions, as well as changes in teachers' epistemological beliefs. Further, we explored the impact of the three teachers' module integration on their students' STEM attitudes and nanotechnology perceptions. The findings indicated that teachers were generally satisfied with the research and course experiences. Further, as a result of RETAIN participation, teachers showed increased nanotechnology content knowledge and knowledge of nanotechnology-related careers. Lastly, three teachers' integration of nanotechnology modules indicated that their students had significantly improved perceptions of nanotechnology's potential coupled with more knowledge of nanotechnology-related careers. The paper concludes with considerations of the quantitative findings in light of teachers' written reflections and author observations of teacher module integration in their classrooms.
The mechanics of corneal deformation and rupture for penetrating injury in the human eye

Penetrating eye injuries are surgical emergencies with guarded visual prognosis. The purpose of the current study was to determine the force required to rupture the cornea with a penetrating object, and to study how this force is affected by the object geometry. Thirty-six human cadaveric eyes from donors of various ages were characterized for diameter, axial length, and pre-test intraocular pressure. In order to investigate the effects of specimen storage time on the tissue response, half of the specimens were tested within two weeks of donor expiration, and half of the specimens were stored at -4°C for 12-18 months. Indenters of three different diameters (1.0, 1.5, and 2.0mm) were lowered into the apex of the cornea until rupture. Resistance to displacement (stiffness), displacement at failure, and the force at failure were determined. Multi-variable regression analysis was used to determine associations of the input variables (indenter size, test speed, and tissue postmortem time) on the mechanics of the tissue response. Twenty-nine of the 36 specimens failed at the indenter location in the cornea, four failed at the limbus, and three failed in the sclera near sites of muscle attachment. The average force at failure caused by the 1.0mm, 1.5mm, and 2.0mm indenters increased from 30.5±5.5N to 40.5±8.3N to 58.2±14.5N, respectively (p<0.002). The force at failure was associated with the donor age (p<0.001), and globe diameter (p<0.041), but was not associated with pre-test intraocular pressure, tissue postmortem time, axial length, or speed of the indenter. This study has quantified the force-displacement and failure response of a large series of human cadaveric eyes subjected to penetrating indentation loads on the cornea. The results provide useful data for characterizing the relationship between corneal rupture and the geometry of a penetrating object.
One Markup to Rule Them All: Taxation by Liquor Pricing Regulation

Commodity taxation often involves uniform tax rates. We use alcohol laws that tax differentiated spirits with a comprehensive uniform markup to evaluate redistribution generated by such simple tax policy. We document preference heterogeneity among consumers, variation in product demand elasticities, and market power among producers with heterogeneous product portfolios. Relative to more flexible product-level markups recognizing demand heterogeneity and strategic price responses of firms, we find that the uniform markup underprices less elastic spirits, implicitly subsidizing low-income and less educated residents. The uniform markup grants additional market power to small specialized firms whose product positioning benefits from the policy.
Implicit LES using adaptive filtering

Abstract In implicit large eddy simulations (ILES) numerical dissipation prevents buildup of small scale energy in a manner similar to the explicit subgrid scale (SGS) models. If spectral methods are used the numerical dissipation is negligible but it can be introduced by applying a low-pass filter in the physical space, resulting in an effective ILES. In the present work we provide a comprehensive analysis of the numerical dissipation produced by different filtering operations in a turbulent channel flow simulated using a non-dissipative, pseudo-spectral Navier–Stokes solver. The amount of numerical dissipation imparted by filtering can be easily adjusted by changing how often a filter is applied. We show that when the additional numerical dissipation is close to the subgrid-scale (SGS) dissipation of an explicit LES the overall accuracy of ILES is also comparable, indicating that periodic filtering can replace explicit SGS models. A new method is proposed, which does not require any prior knowledge of a flow, to determine the filtering period adaptively. Once an optimal filtering period is found, the accuracy of ILES is significantly improved at low implementation complexity and computational cost. The method is general, performing well for different Reynolds numbers, grid resolutions, and filter shapes.
The Impact of TOM on Prices in the US Housing Market

Search theory shows that real property prices and marketing durations are simultaneously determined and positively related. Yet, empirical studies find positive, negative, and insignificant parameter estimates on the time-on-the-market (TOM) variable in price models. Using a dataset well suited to the research question, this article investigates reasons for the divergence between the theoretical and empirical results. Our test equations examine the quality of instrumental variables, severe overpricing, atypicality, structure quality, loss aversion, market tightness as well as measures unique to our data such as sellers’ income levels, reasons for sale, and urgency. We find that weak instrumental variables account for the varied empirical relations between transaction prices and TOM.
Indicator Variables for Optimal Policy under Asymmetric Information

The optimal weights on indicators in models with partial information about the state of the economy and forward-looking variables are derived and interpreted, both for equilibria under discretion and under commitment. The private sector is assumed to have more information about the state of the economy than the policymaker. Certainty equivalence holds for the optimal reaction function in state-space form :t he coefficients are independent of the degree of uncertainty about the state of the economy. However, in the case of commitment, certainty equivalence does not hold for the reaction function in integrative form: the policy instrument cannot be written as a distributed lag of past estimates of past states of the economy. Instead, optimal policy will generally depend on new estimates of past states of the economy, and in ways that depend on the information structure. Furthermore, the usual separation principle does not hold, since the estimation of the state of the economy is not independent of optimization and is, in general, quite complex. We present a general characterization of optimal filtering and control in settings of this kind, and discuss an application of our methods to the problem of the optimal use of “real-time” macroeconomic data in the conduct of monetary policy.
MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

Background and purpose Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Methods Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. Results In the Johns Hopkins study, MIMoSA yielded average Sorensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. Conclusion MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study.
Divergent responses to kisspeptin in children with delayed puberty.

Background The neuropeptide kisspeptin stimulates luteinizing hormone (LH) secretion in healthy adults but not in adults with idiopathic hypogonadotropic hypogonadism. We hypothesized that, in children presenting with delayed or stalled puberty, kisspeptin would elicit LH secretion in those children found on detailed nighttime neuroendocrine profiling to have evidence of emerging reproductive endocrine function. Methods Eleven boys and four girls were admitted overnight to assess LH secretion at baseline, after a single intravenous bolus of kisspeptin, and after a single intravenous bolus of gonadotropin-releasing hormone (GnRH). Subjects then received exogenous pulsatile GnRH for 6 days and returned for a second visit to measure responses to kisspeptin and GnRH after this pituitary "priming." Responses to kisspeptin and GnRH were also measured in 5 healthy men. Results Of the 15 children with delayed/stalled puberty, 6 exhibited at least one spontaneous LH pulse overnight; all of these subjects had clear responses to kisspeptin, as did one additional subject. Seven subjects had no response to kisspeptin, and one subject exhibited an intermediate response. In the children who responded to kisspeptin, the responses had features comparable to those of adult men. Conclusion In this first report of kisspeptin administration to pediatric subjects to our knowledge, children with delayed/stalled puberty showed a wide range of responses, with some showing a robust response and others showing little to no response. Further follow-up will determine whether responses to kisspeptin predict future pubertal entry for children with delayed puberty. Trial registration ClinicalTrials.gov NCT01438034 and NCT01952782. Funding NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD043341, R01 HD090071, P50 HD028138), NIH National Center for Advancing Translational (UL1 TR001102), NIH National Institute of Diabetes and Digestive and Kidney Diseases (T32 DK007028), the Massachusetts General Hospital Executive Committee on Research Fund for Medical Discovery, Harvard Catalyst, Doris Duke Charitable Foundation (award 2013110), Charles H. Hood Foundation, Robert and Laura Reynolds MGH Research Scholar Program, and Harvard University. These funding sources had no role in the design of this study and did not have any role in conducting the study, analyses, interpretation of the data, or the decision to submit results.
Fading affect bias: improving the trade-off between accuracy and efficiency in feature clustering

We present a fast and accurate center-based, single-pass clustering method, with main focus on improving the trade-off between accuracy and speed in computer vision problems, such as creating visual vocabularies. We use a stochastic mean-shift procedure to seek the local density peaks within a single pass of the data. We also present a dynamic kernel generation along with a density test procedure that finds the most promising kernel initializations. In our algorithm, we use two data structures, namely a dictionary of permanent kernels, and a short memory that is used to determine emerging kernels to be maintained and outliers to be discarded. We further develop a hierarchical realization of the algorithm such that it executes faster than most stream cluster algorithms, and the resulting tree serves as an efficient data structure to boost encoding procedure. We use our method for learning low-level image features on the fly, from unmanned aerial vehicle cameras, aiming at vision-driven maneuvering with reduced computational cost. In our experiments, we make extensive comparisons with popular clustering algorithms, with respect to accuracy and efficiency. Our algorithm showed improved accuracy and speed on datasets with sufficient cluster patterns. With noisy visual features, where natural clusters present inherent challenges (intra-cluster variability and inter-cluster similarities), we achieved high accuracy, compared to algorithms of higher complexity, while maintaining high efficiency: Only one method from the competition achieved lower run times, though with lower accuracy.
Leveraging Compute Clusters for Large-Scale Parametric Screens of Reaction-Diffusion Systems

Reaction-diffusion (RD) models are widely used to study the spatio-temporal evolution of pattern formation during development. Nonlinear RD models are often analytically intractable, and require numerical solution methods. Interrogation of RD models for a large physiological range of parameters covers many orders of magnitude, establishing situations where solutions are stiff and solvers fail to provide accurate results to the time-dependent problem. The spatial dependence of these parameters, and the nonlinearity of the underlying dynamics, impose additional challenges. We developed an efficient approach for simulating stiff RD models of pattern formation and we used supercomputer clusters to carry out a large screen of spatially varying parameters. The proposed approach generated data for screening of RD systems within a reasonable amount of time (a few days), which scales down further if additional cluster nodes are available. The approaches outlined herein are applicable to any systems biology problem requiring numerical approximation of RD equations with spatially non-uniform properties and stiff nonlinear reactions.
Do bicycling experiences and exposure influence bicycling skills and attitudes? Evidence from a bicycle-friendly university

Life changes are often associated with changes in travel behavior, due to a break in habitual travel cues and the introduction of a novel travel context. Universities provide a particularly appropriate setting to examine how these life changes can bring about changes in travel attitudes, 27 norms, and skills – which together form a psychological construct called “motility” that describes the capability for travel. In this study, I pool data from seven years of the University of California, Davis’ annual campus travel survey to create a longitudinal panel, and use a retrospective survey to collect the bicycling behaviors, attitudes, and skills of undergraduates every year since they graduated from high school. I find that, on average, UCD undergraduates’ pro-bicycling attitudes decrease slightly over time while bicycling skills increase substantially throughout college. I then use the retrospective panel data to estimate a statistical model to analyze the influence of bicycling exposure and experiences on skills and attitudes. I find that riding a bicycle at any point during college increases both pro-bicycling attitudes and bicycling skills, while exposure to high levels of bicycling appears not to influence attitudes or skills. This study provides confirmatory evidence for the motility approach and suggests possible policy avenues, such as incentivizing short-term bicycle use in order to shift perceptions and attitudes about bicycling, with the intent of fostering a positive feedback cycle between greater bicycling attitudes and skills and increased bicycle use.
Degree Fluctuations and the Convergence Time of Consensus Algorithms

We consider a consensus algorithm in which every node in a sequence of undirected, B-connected graphs assigns equal weight to each of its neighbors. Under the assumption that the degree of each node is fixed (except for times when the node has no connections to other nodes), we show that consensus is achieved within a given accuracy e on n nodes in time B+4n3 Bln(2n/e). Because there is a direct relation between consensus algorithms in time-varying environments and in homogeneous random walks, our result also translates into a general statement on such random walks. Moreover, we give a simple proof of a result of Cao, Spielman, and Morse that the worst case convergence time becomes exponentially large in the number of nodes n under slight relaxation of the degree constancy assumption.
Coreset-Based Neural Network Compression

We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression. Our method requires no retraining, is easy to implement, and obtains state-of-the-art compression performance across a wide variety of CNN architectures. Coupled with quantization and Huffman coding, we create networks that provide AlexNet-like accuracy, with a memory footprint that is 832\(\times \) smaller than the original AlexNet, while also introducing significant reductions in inference time as well. Additionally these compressed networks when fine-tuned, successfully generalize to other domains as well.
An epidemiological review of diet and cutaneous malignant melanoma

Incidence of cutaneous malignant melanoma has continued to rise despite public efforts to promote sun protection behaviors among populations at risk. However, dietary factors may also affect the development of melanoma. In the past few decades, findings from epidemiologic and experimental research have linked consumption of several foods and other nutrients to the risk of melanoma. Caffeine has been associated with a lower risk of melanoma, and citrus fruits and alcohol with increased risk. Associations between polyunsaturated fatty acid, niacin/nicotinamide, folate, and vitamin D with melanoma remain controversial. Diet likely influences melanoma development through several potential mechanisms, such as enhancing UV-induced apoptosis and increasing photosensitivity. We conducted a narrative review to summarize recent epidemiologic studies of diet and melanoma based on published literature. Given the high prevalence of the food items and nutrients covered in this review and the decades-long rising melanoma incidence worldwide, the associations we discuss may have important public health implications in terms of reducing melanoma incidence through dietary modification. Cancer Epidemiol Biomarkers Prev; 27(10); 1115-22. ©2018 AACR.
Collapse and revival oscillation in Double Jaynes–Cummings model

We develop a systematic method of solving two noninteracting Jaynes–Cummings models by using the dressed state formalism in Hilbert space HAB(2⊗2). It is shown that such model, called Double Jaynes–Cummings model (D-JCM), can be exactly solved if we take the initial bare state as the linear superposition of two Bell states. The collapse and revival oscillation, which is the standard trait of typical Jaynes–Cummings model, can be recovered if we make measurement at each local sites. Some consequence of the entanglement-induced dressing is discussed.
Immunohistochemical characteristics of renomedullary interstitial cell tumor: a study of 41 tumors with emphasis on differential diagnosis of mesenchymal neoplasms.

Summary Renomedullary interstitial cell tumors (RMICTs) are almost always incidentally identified either at autopsy or upon resection of the kidney for other reasons. However, rare cases that are large, resulting in a clinical mass, have been reported. The immunohistochemical phenotype of usual, incidental RMICT using modern soft tissue tumor markers is largely unknown, however, providing little information to aid in classification of larger or atypical tumors. We retrieved 41 RMICTs from 36 patients and studied pathologic characteristics including morphology, immunohistochemistry (S100, keratin AE1/AE3, smooth muscle actin, desmin, estrogen and progesterone receptors, calponin, CD34, CD35), and histochemical staining. Data collected included age, sex, tumor size, laterality, and indication for kidney examination. RMICTs (n = 41) were identified in 23 men and 13 women, with a mean age of 57 years (range, 24-83 years); tumor sizes ranged from less than 1 to 13 mm (median, 4 mm). Kidneys were resected for 32 tumors, 1 chronic pyelonephritis, 1 trauma, and 2 autopsies. All (41; 100%) had entrapped renal tubules, 5 (12%) of which included cystic or dilated tubules. Most (35; 85%) had collagenous fibers, all of which were negative for Congo red. RMICT demonstrates a largely negative immunohistochemical phenotype with weak-to-moderate labeling for smooth muscle actin and calponin that is substantially less than myofibroblastic lesions. Positive staining for estrogen and progesterone receptors is common (61%), which could overlap with mixed epithelial and stromal tumor and other entities; however, staining is typically weak. CD34 is usually negative, with occasional weak labeling, in contrast to solitary fibrous tumor.
IS MORE COMPETITION ALWAYS BETTER? AN EXPERIMENTAL STUDY OF EXTORTIONARY CORRUPTION

Using a laboratory experiment, we assess whether increasing competition among public officials reduces extortionary corruption. We find that increasing the number of providers has no effect on bribe demands when citizens' search costs are high, but it increases corruption when search costs are low. The effect is absent in a parallel setting framed as a standard market, which we attribute to citizens using a nonsequential search strategy as opposed to sequential search in the corruption setting. We conclude that efforts to reduce search costs, such as infrastructure investments, are preferable to anti‐corruption policies aimed at increasing the number of providers. (JEL D73, D49, C91)
Active Learning based on Data Uncertainty and Model Sensitivity

Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether the robot has the necessary knowledge to perform the task. Failing to detect missing information often leads to abrupt movements or to collisions with the environment. Active learning can quantify the uncertainty of performing the task and, in general, locate regions of missing information. We introduce a novel algorithm for active learning and demonstrate its utility for generating smooth trajectories. Our approach is based on deep generative models and metric learning in latent spaces. It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i.e., transitions that lead to abrupt changes in the movement of the robot. When non-smooth transitions are detected, our algorithm asks for an additional demonstration from that specific region. The newly acquired knowledge modifies the data manifold and allows for learning a latent representation for generating smooth movements. We demonstrate the efficacy of our approach on generalising elementary skills, transitioning across different skills, and implicitly avoiding collisions with the environment. For our experiments, we use a simulated pendulum where we observe its motion from images and a 7-DoF anthropomorphic arm.
A unified framework for joint mobility prediction and object profiling of drones in UAV networks

In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, particularly in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this study, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs, to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g., rotatory and fixed-wing UAVs) without prior knowledge regarding these classes. This method is flexible in admitting new object types with unknown mobility profiles, and is thereby applicable to emerging flying ad-hoc networks (FANETs) with heterogeneous nodes.
Eliminating Unstable Tests in Floating-Point Programs

Round-off errors arising from the difference between real numbers and their floating-point representation cause the control flow of conditional floating-point statements to deviate from the ideal flow of the real-number computation. This problem, which is called test instability, may result in a significant difference between the computation of a floating-point program and the expected output in real arithmetic. In this paper, a formally proven program transformation is proposed to detect and correct the effects of unstable tests. The output of this transformation is a floating-point program that is guaranteed to return either the result of the original floating-point program when it can be assured that both its real and its floating-point flows agree or a warning when these flows may diverge. The proposed approach is illustrated with the transformation of the core computation of a polygon containment algorithm developed at NASA that is used in a geofencing system for unmanned aircraft systems.
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking

This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.
Style investing and the ICAPM

Empirical multifactor models of excess returns are theoretically grounded in Merton’sICAPM. Merton modeled investors who maximize expected utility of their own consumption. Multiplepriced factors arise as “hedge portfolios” most closely correlated with state variables that driveintertemporal changes in the investment opportunity set. It is puzzling that empirically useful factorshave not been convincingly identified as those portfolios, despite massive e ort. But the majorityof asset demand now arises from style investors, i.e. institutionally managed funds that try to eithermeet (index funds) or beat (actively managed) returns from style-specific benchmark portfolios. Sowe modify Merton’s derivation to incorporate the aggregate demands derived from style investors’di erent objective functions. In addition to resolving the aforementioned puzzle, we show that thisstyle investing version of the ICAPM is more consistent with recent empirical evidence documentingcomovement among assets held in a style benchmark. Finally, the model casts doubt on the widespreadbelief that individual investors will necessarily benefit from funds with positive multifactor alpha.
Risk premia in crude oil futures prices

If commercial producers or financial investors use futures contracts to hedge against commodity price risk, the arbitrageurs who take the other side of the contracts may receive compensation for their assumption of nondiversifiable risk in the form of positive expected returns from their positions. We show that this interaction can produce an affine factor structure to commodity futures prices, and develop new algorithms for estimation of such models using unbalanced data sets in which the duration of observed contracts changes with each observation. We document significant changes in oil futures risk premia since 2005, with the compensation to the long position smaller on average in more recent data. This observation is consistent with the claim that index-fund investing has become more important relative to commerical hedging in determining the structure of crude oil futures risk premia over time.
Distributed Stochastic Optimization via Adaptive SGD

Stochastic convex optimization algorithms are the most popular way to train machine learning models on large-scale data. Scaling up the training process of these models is crucial, but the most popular algorithm, Stochastic Gradient Descent (SGD), is a serial method that is surprisingly hard to parallelize. In this paper, we propose an efficient distributed stochastic optimization method by combining adaptivity with variance reduction techniques. Our analysis yields a linear speedup in the number of machines, constant memory footprint, and only a logarithmic number of communication rounds. Critically, our approach is a black-box reduction that parallelizes any serial online learning algorithm, streamlining prior analysis and allowing us to leverage the significant progress that has been made in designing adaptive algorithms. In particular, we achieve optimal convergence rates without any prior knowledge of smoothness parameters, yielding a more robust algorithm that reduces the need for hyperparameter tuning. We implement our algorithm in the Spark distributed framework and exhibit dramatic performance gains on large-scale logistic regression problems.
Structural basis for antiarrhythmic drug interactions with the human cardiac sodium channel

The human voltage-gated sodium channel, hNaV1.5, is responsible for the rapid upstroke of the cardiac action potential and is target for antiarrhythmic therapy. Despite the clinical relevance of hNaV1.5-targeting drugs, structure-based molecular mechanisms of promising or problematic drugs have not been investigated at atomic scale to inform drug design. Here, we used Rosetta structural modeling and docking as well as molecular dynamics simulations to study the interactions of antiarrhythmic and local anesthetic drugs with hNaV1.5. These calculations revealed several key drug binding sites formed within the pore lumen that can simultaneously accommodate up to two drug molecules. Molecular dynamics simulations identified a hydrophilic access pathway through the intracellular gate and a hydrophobic access pathway through a fenestration between DIII and DIV. Our results advance the understanding of molecular mechanisms of antiarrhythmic and local anesthetic drug interactions with hNaV1.5 and will be useful for rational design of novel therapeutics.
Non-canonical Drosophila X chromosome dosage compensation and repressive topologically associated domains

In animals with XY sex chromosomes, X-linked genes from a single X chromosome in males are imbalanced relative to autosomal genes. To minimize the impact of genic imbalance in male Drosophila, there is a dosage compensation complex (MSL) that equilibrates X-linked gene expression with the autosomes. There are other potential contributions to dosage compensation. Hemizygous autosomal genes located in repressive chromatin domains are often derepressed. If this homolog-dependent repression occurs on the X, which has no pairing partner, then derepression could contribute to male dosage compensation. We asked whether different chromatin states or topological associations correlate with X chromosome dosage compensation, especially in regions with little MSL occupancy. Our analyses demonstrated that male X chromosome genes that are located in repressive chromatin states are depleted of MSL occupancy; however, they show dosage compensation. The genes in these repressive regions were also less sensitive to knockdown of MSL components. Our results suggest that this non-canonical dosage compensation is due to the same transacting derepression that occurs on autosomes. This mechanism would facilitate immediate compensation during the evolution of sex chromosomes from autosomes. This mechanism is similar to that of C. elegans, where enhanced recruitment of X chromosomes to the nuclear lamina dampens X chromosome expression as part of the dosage compensation response in XX individuals.
Memory experts' beliefs about repressed memory.

ABSTRACTWhat we believe about how memory works affects the decisions we make in many aspects of life. In Patihis, Ho et al. [Patihis, L., Ho, L. Y., Tingen, I. W., Lilienfeld, S. O., & Loftus, E. F. (2014). Are the "memory wars" over? A scientist-practitioner gap in beliefs about repressed memory. Psychological Science, 25, 519-530.], we documented several group's beliefs on repressed memories and other aspects of how memory works. Here, we present previously unreported data on the beliefs of perhaps the most credible minority in our dataset: memory experts. We provide the statistics and written responses of the beliefs for 17 memory experts. Although memory experts held similarly sceptical beliefs about repressed memory as other research-focused groups, they were significantly more sceptical about repressed memory compared to practitioners, students and the public. Although a minority of memory experts wrote that they maintained an open mind about repressed memories - citing research such as retrieval inhibition - all of the memory experts emphasised the dangers of memory distortion.
Can Bitcoin become money? The monetary rule problem

The growing literature on Bitcoin can be divided into two groups. One performs an economic analysis of Bitcoin focusing on its monetary characteristics. The other one takes a financial look at the price of Bitcoin. Interestingly, both of these groups have not given much more than passing comments to the problem of whether or not Bitcoin has the right monetary rule in order to become a well‐established currency. This paper argues that Bitcoin in particular, and cryptocurrencies in general, do not have a good monetary rule and that this shortcoming seriously limits its prospect of becoming widely used money.
USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL

With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches.