cssi/SciSciGPT-SciSciNet huggingface dataset:
Plasma ion stratification by weak planar shocks

We derive fluid equations for describing steady-state planar shocks of a moderate strength ( 0<M−1≲1 with M being the shock Mach number) propagating through an unmagnetized quasineutral collisional plasma comprising two separate ion species. In addition to the standard fluid shock quantities, such as the total mass density, mass-flow velocity, and electron and average ion temperatures, the equations describe shock stratification in terms of variations in the relative concentrations and temperatures of the two ion species along the shock propagation direction. We have solved these equations analytically for weak shocks ( 0<M−1≪1), with the results depending on M, ratios of the ion masses and charges, and the upstream mass fraction of one of the ion species. These analytical results are instrumental for gaining understanding of the behavior of weak shocks, and they have been used to verify kinetic simulations of shocks in multi-ion plasmas.
Effect Of A “No Superuser Opioid Prescription” Policy On ED Visits And Statewide Opioid Prescription

Introduction: The U.S. opioid epidemic has highlighted the need to identify patients at riskof opioid abuse and overdose. We initiated a novel emergency department- (ED) basedinterventional protocol to transition our superuser patients from the ED to an outpatient chronicpain program. The objective was to evaluate the protocol’s effect on superusers’ annual ED visits.Secondary outcomes included a quantitative evaluation of statewide opioid prescriptions for thesepatients, unique prescribers of controlled substances, and ancillary testing. Methods: Patients were referred to the program with the following inclusion criteria: ≥ 6 visitsper year to the ED; at least one visit identified by the attendi ng physician as primarily driven byopioid-seeking behavior; and a review by a committee comprising ED administration and casemanagement. Patients were referred to a pain management clinic and informed that they wouldno longer receive opioid prescriptions from visits to the ED for chronic pain complaints. Electronicmedical record (EMR) alerts notified ED providers of the patient’s referral at subsequent visits. Weanalyzed one year of data pre- and post-referral. Results: A total of 243 patients had one year of data post-referral for analysis. Median annualED visits decreased from 14 to 4 (58% decrease, 95% CI [50 to 66]). We also found statisticallysignificant decreases for these patients’ state prescription drug monitoring program (PDMP) opioidprescriptions (21 to 13), total unique controlled-substance prescribers (11 to 7), computed tomographyimaging (2 to 0), radiographs (5 to 1), electrocardiograms (12 to 4), and labs run (47 to 13). Conclusion: This program and the EMR-based alerts were successful at decreasing local EDvisits, annual opioid prescriptions, and hospital resource allocation for this population of patients.There is no evidence that these patients diverted their visits to neighboring EDs after beinginformed that they would not receive opioids at this hospital, as opioid prescriptions obtained bythese patients decreased on a statewide level. This implies that individual ED protocols can havesignificant impact on the behavior of patients. [West J Emerg Med. 2017;18(5)894-902.]
Microwave-induced dephasing in one-dimensional metal wires.

We report on the effect of monochromatic microwave (MW) radiation on the weak-localization corrections to the conductivity of quasi-one-dimensional silver wires. Because of the improved electron cooling in the wires, the MW-induced dephasing is observed without a concomitant overheating of electrons over wide ranges of the MW power ${P}_{\mathrm{MW}}$ and frequency $f$. The observed dependences of the conductivity and MW-induced dephasing rate on ${P}_{\mathrm{MW}}$ and $f$ are in agreement with the theory by Altshuler, Aronov, and Khmelnitsky [Solid State Commun. 39, 619 (1981)]. Our results suggest that in the low-temperature experiments with 1D wires, saturation of the temperature dependence of the dephasing time can be caused by an MW electromagnetic noise with a sub-pW power.
Emerging Treatments for Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis

This review discusses completed phase II randomized clinical trials with high-quality published results for compounds that demonstrate effects on nonalcoholic steatohepatitis histology (obeticholic acid, elafibranor, and liraglutide). The authors also review the available preliminary data on cenicriviroc and selonsertib, with or without simtuzumab's phase II studies. Finally, the authors briefly discuss compounds that have been tested but did not achieve the primary end point of histologic improvement and appeared in high-quality published articles (cysteamine bitartrate and long-chain polyunsaturated fatty acids).
Predicting cancer outcomes from histology and genomics using convolutional networks

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.
TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data.

Motivation The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC. Results We develop TCGA-assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research. Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. Availability and implementation http://www.compgenome.org/TCGA-Assembler/ or https://github.com/compgenome365/TCGA-Assembler-2. Contact zhuyitan@gmail.com or koaeraser@gmail.com. Supplementary information Supplementary data are available at Bioinformatics online.
Multiscale Simulation of Surface Nanostructure Effect on Bubble Nucleation

Effects of nanostructured defects of copper solid surface on the bubble growth in liquid argon have been investigated through a hybrid atomistic-continuum method. The same solid surfaces with five different nanostructures, namely, wedge defect, deep rectangular defect (R-I), shallow rectangular defect (R-II), small rectangular defect (R-III) and no defect, have been modeled at molecular level. The liquid argon is placed on top of the hot solid copper with superheat of 30 K after equilibration is achieved with CFD-MD coupled simulation. Phase change of argon on five nanostructures has been observed and analyzed accordingly. The results showed that the solid surface with wedge defect tends to induce a nano-bubble relatively more easily than the others, and the larger the size of the defect is the easier the bubble generate.
Generating and sharing of market intelligence in sales teams: an economic social network perspective

AbstractIn this research, we take a multimethod approach to shed light on the potential costs to sales teams that generate and share market intelligence (MI). First, we introduce an analytical model to propose the respective levels of effort that sales managers, experts, and team members spend generating and sharing MI. To test our propositions, we utilize social network data from 40 independent, business-to-business (B2B) sales teams, representing 287 salespeople. Interestingly, our results support the premise that team members become dependent (reduce MI efforts) when their sales manager or team expert shares MI among the team. We term this a “sharing tax” that sales managers and team experts pay when they share MI. Consequently, sales managers demonstrate greater MI-generation efforts the more they share MI. We also find that experts who share more (less) also show greater (lesser) MI-generation efforts, but only for teams where sales managers share low (high) levels of MI. In summary, our research inn...
Managerial ability and firm risk-taking behavior

In this study, we show that managerial heterogeneity plays an important role in firm decisions. Our view is that in addition to the effects of previously examined determinants, firm decisions are affected not just by the managers’ explicit mandate to maximize firm value, but also by the ability of the manager in managing the firm. We find that high-ability managers and low-ability managers have opposite effects on firm behavior and firm value. High-ability managers are receptive to risk-taking whereas low-ability managers refrain from risk-taking. High-ability managers cut capital expenditures but spend significantly more on research and development projects; low-ability managers reduce both capital expenditures and research and development expenses significantly. High-ability managers are associated with higher levels of firm focus than low-ability managers. Managerial ability is negatively associated with firm leverage. In addition, our results show that high-ability managers are associated with increases in firm value whereas low-ability managers are associated with decreases in firm value.
Predicting viscous-range velocity gradient dynamics in large-eddy simulations of turbulence

The detailed dynamics of small-scale turbulence are not directly accessible in large-eddy simulations (LES), posing a modelling challenge, because many micro-physical processes such as deformation of aggregates, drops, bubbles and polymers dynamics depend strongly on the velocity gradient tensor, which is dominated by the turbulence structure in the viscous range. In this paper, we introduce a method for coupling existing stochastic models for the Lagrangian evolution of the velocity gradient tensor with coarse-grained fluid simulations to recover small-scale physics without resorting to direct numerical simulations (DNS). The proposed approach is implemented in LES of turbulent channel flow and detailed comparisons with DNS are carried out. An application to modelling the fate of deformable, small (sub-Kolmogorov) droplets at negligible Stokes number and low volume fraction with one-way coupling is carried out and results are again compared to DNS results. Results illustrate the ability of the proposed model to predict the influence of small-scale turbulence on droplet micro-physics in the context of LES.
The CRISPR Patent Landscape: Past, Present, and Future

Abstract The development of CRISPR depends, in part, on the patents—past, present, and future—covering it. As for the past, the origins of the CRISPR patent landscape predate its use as a gene editing technology. Fundamental patents covering CRISPR-Cas9 as a genomic editing system did not first arise until 2012; they sparked the now canonical dispute between the University of California and the Broad Institute. The present dispute has not stopped widespread licensing of critical patents, however, bringing with it an explosion of research from both academic and commercial sectors. Whether this broad availability will persist in the future remains uncertain. The ease and reliability of CRISPR threatens many future patents as being “obvious.” Nor is it clear how academic scientists and technology transfer offices will respond to the patent dispute. Like the technology itself, the future of the CRISPR patent landscape depends on researchers and their institutions.
Disparity between the programmatic views and the user perceptions of mobile apps

User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty — B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives.
STRUCTURAL CHANGE OR LOGICAL INCREMENTALISM? TURBULENCE IN THE GLOBAL MEAT SYSTEM

In the last ten years the global meat industry has encountered numerous critical events related to food safety and food quality. These events in turn have caused the industry to re-evaluate how the meat supply chain functions and how to service the new social attributes demanded in the market place. This paper uses a unique methodology, shedding light on why and how the US meat industry is evolving and why its evolutionary path may differ from that which is occurring in Europe.
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

We introduce mixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS.
Ecological control of nitrite in the upper ocean

Microorganisms oxidize organic nitrogen to nitrate in a series of steps. Nitrite, an intermediate product, accumulates at the base of the sunlit layer in the subtropical ocean, forming a primary nitrite maximum, but can accumulate throughout the sunlit layer at higher latitudes. We model nitrifying chemoautotrophs in a marine ecosystem and demonstrate that microbial community interactions can explain the nitrite distributions. Our theoretical framework proposes that nitrite can accumulate to a higher concentration than ammonium because of differences in underlying redox chemistry and cell size between ammonia- and nitrite-oxidizing chemoautotrophs. Using ocean circulation models, we demonstrate that nitrifying microorganisms are excluded in the sunlit layer when phytoplankton are nitrogen-limited, but thrive at depth when phytoplankton become light-limited, resulting in nitrite accumulation there. However, nitrifying microorganisms may coexist in the sunlit layer when phytoplankton are iron- or light-limited (often in higher latitudes). These results improve understanding of the controls on nitrification, and provide a framework for representing chemoautotrophs and their biogeochemical effects in ocean models.
Local existence, lower mass bounds, and a new continuation criterion for the Landau equation

Abstract We consider the spatially inhomogeneous Landau equation with soft potentials. First, we establish the short-time existence of solutions, assuming the initial data has sufficient decay in the velocity variable and regularity (no decay assumptions are made in the spatial variable). Next, we show that the evolution instantaneously spreads mass throughout the domain. The resulting lower bounds are sub-Gaussian, which we show is optimal. The proof of mass-spreading is based on a stochastic process, and makes essential use of nonlocality. By combining this theorem with prior results, we derive two important applications: C ∞ -smoothing, even for initial data with vacuum regions, and a continuation criterion (the solution can be extended as long as the mass and energy densities stay bounded from above). This is the weakest condition known to prevent blow-up. In particular, it does not require a lower bound on the mass density or an upper bound on the entropy density.
Advancing Diabetes Management in Adolescents: Comparative Effectiveness of Mobile Self-Monitoring Blood Glucose Technology and Family-Centered Goal Setting

Background As adolescents gain autonomy, it remains important for parents to be involved with diabetes management to avoid deterioration in glycemic control. Technologies for self-monitoring of blood glucose (SMBG) allow for remote monitoring in real-time by parents. This research compared 3 strategies for improving SMBG and diabetes self-care in the short-term. These strategies were: (1) health information technology (HIT)-enhanced blood glucose meter that shared blood glucose data among patients, their parent, and care providers, and allowed for text messaging; (2) family-centered goal setting; and (3) a combination of (1) and (2). Methods One hundred twenty-eight participants enrolled; 97 adolescent-parent pairs attended clinic at 3-month intervals during the 6-month intervention. Differences between treatment groups were evaluated using analysis of variance (ANOVAs) for continuous variables and χ2 tests for frequencies. Within patient changes were evaluated using paired t tests. Results Participants in the HIT-enhanced SMBG group had no change in mean glycosylated hemoglobin (HbA1c). Participants assigned to family-centered goal setting had a non-significant decrease in HbA1c of −0.3% (P = .26) from baseline to 6 months. Participants in the combined approach had a significant decrease in HbA1c of −0.6% (P = .02) from baseline to 3 months, but the decrease of −0.4% at 6 months was non-significant (P = .51). The change in HbA1c from baseline to 3 months was greater for the combined approach than for the HIT-enhanced SMBG (P = .05) or family-centered goal setting (P = .01). Conclusions Our data suggest that utilizing the family-centered goal setting strategy when implementing HIT-enhanced diabetes technology deserves further study.
Dual method use among long-acting reversible contraceptive users.

AbstractObjective: To compare rates of dual method use (concurrent use of condoms and an effective method of contraception) in long-acting reversible contraceptive (LARC) and non-LARC hormonal contraceptive users, and to determine factors associated with dual method use.Methods: We conducted a secondary analysis of the Contraceptive CHOICE Project, an observational, prospective cohort study of 9256 women in St. Louis, MO, USA. Our sample included 6744 women who initiated a contraceptive method within 3 months of enrollment, continued use at 6 months post-enrollment, and responded regarding dual method use. Our primary outcome was the rate of dual method use at 6 months post-enrollment.Results: Dual method use was reported by 32% of LARC and 45% of non-LARC hormonal contraceptive users (p < .01). After adjusting for other covariates and comparing to non-LARC hormonal contraceptive users, LARC users were less likely to report dual method use (RRadj 0.76, 95% CI 0.70–0.83). Factors associated with dual metho...
A bony defect of the sphenoid air sinus found in male cadaver: a case report

The sphenoid sinus one of the posterior group of sinus, considered one of the most difficult sinuses due to its deep location in the skull. The interest in the understanding of the anatomy of the sphenoid sinus increased markedly during the last decades due to the development of endoscopic transsphenoidal approaches to the intracranial structures. The important anatomical relations of the sphenoid sinus make it one of the difficult and challenging structures for the surgeons. In this case report,we present one of the anatomical variations that was found during cadaveric dissection of the head. In this case we found abnormal bony defect in the lateral wall of the sinus,the defect closed just by the lining mucosa of the sinus and the dura from its intracranial side. A point may be of clinical importance to the related nearby structures.
Holmium Laser Enucleation of the Prostate.

Abstract Introduction: Holmium laser enucleation of the prostate (HoLEP) offers superior voiding outcomes to traditional transurethral resection and less morbidity than open simple prostatectomy. L...
Approximate Capacity of Fast Fading Interference Channels With no Instantaneous CSIT

We develop a characterization of fading models, which assigns a number called logarithmic Jensen’s gap to a given fading model. We show that as a consequence of a finite logarithmic Jensen’s gap, an approximate capacity region can be obtained for fast fading interference channels (FF-ICs) for several scenarios. We illustrate three instances where a constant capacity gap can be obtained as a function of the logarithmic Jensen’s gap. First, for an FF-IC with neither feedback nor instantaneous channel state information at transmitter (CSIT), if the fading distribution has finite logarithmic Jensen’s gap, we show that a rate-splitting scheme based on the average interference-to-noise ratio can achieve its approximate capacity. Second, we show that a similar scheme can achieve the approximate capacity of FF-IC with feedback and delayed CSIT, if the fading distribution has finite logarithmic Jensen’s gap. Third, when this condition holds, we show that point-to-point codes can achieve approximate capacity for a class of FF-ICs with feedback. We prove that the logarithmic Jensen’s gap is finite for common fading models, including Rayleigh and Nakagami fading, thereby obtaining the approximate capacity region of FF-IC with these fading models.
Reversal of loss of bone mass in old mice treated with mefloquine

Abstract Aging is accompanied by imbalanced bone remodeling, elevated osteocyte apoptosis, and decreased bone mass and mechanical properties; and improved pharmacologic approaches to counteract bone deterioration with aging are needed. We examined herein the effect of mefloquine, a drug used to treat malaria and systemic lupus erythematosus and shown to ameliorate bone loss in glucocorticoid-treated patients, on bone mass and mechanical properties in young and old mice. Young 3.5-month-old and old 21-month-old female C57BL/6 mice received daily injections of 5 mg/kg/day mefloquine for 14 days. Aging resulted in the expected changes in bone volume and mechanical properties. In old mice mefloquine administration reversed the lower vertebral cancellous bone volume and bone formation; and had modest effects on cortical bone volume, thickness, and moment of inertia. Mefloquine administration did not change the levels of the circulating bone formation markers P1NP or alkaline phosphatase, whereas levels of the resorption marker CTX showed trends towards increase with mefloquine treatment. In addition, and as expected, aging bones exhibited an accumulation of active caspase3-expressing osteocytes and higher expression of apoptosis-related genes compared to young mice, which were not altered by mefloquine administration at either age. In young animals, mefloquine induced higher periosteal bone formation, but lower endocortical bone formation. Further, osteoclast numbers were higher on the endocortical bone surface and circulating CTX levels were increased, in mefloquine- compared to vehicle-treated young mice. Consistent with this, addition of mefloquine to bone marrow cells isolated from young mice led to increased osteoclastic gene expression and a tendency towards increased osteoclast numbers in vitro. Taken together our findings identify the age and bone-site specific skeletal effects of mefloquine. Further, our results highlight a beneficial effect of mefloquine administration on vertebral cancellous bone mass in old animals, raising the possibility of using this pharmacologic inhibitor to preserve skeletal health with aging.
Where Does Titan Sand Come From: Insight From Mechanical Properties of Titan Sand Candidates

Extensive equatorial linear dunes exist on Titan, but the origin of the sand, which appears to be organic, is unknown. We used nanoindentation to study the mechanical properties of a few Titan sand candidates, several natural sands on Earth, and common materials used in the Titan Wind Tunnel, to understand the mobility of Titan sand. We measured the elastic modulus (E), hardness (H), and fracture toughness (Kc) of these materials. Tholin's elastic modulus (10.4+/-0.5 GPa) and hardness (0.53+/-0.03 GPa) are both an order of magnitude smaller than silicate sand, and is also smaller than the mechanically weak white gypsum sand. With a magnitude smaller fracture toughness (Kc=0.036+/-0.007 MPa-m^(1/2)), tholin is also much more brittle than silicate sand. This indicates that Titan sand should be derived close to the equatorial regions where the current dunes are located, because tholin is too soft and brittle to be transported for long distances.
Visualization of the type III secretion mediated Salmonella -host cell interface using cryo-electron tomography.

Many important gram-negative bacterial pathogens use highly sophisticated type III protein secretion systems (T3SSs) to establish complex host-pathogen interactions. Bacterial-host cell contact triggers the activation of the T3SS and the subsequent insertion of a translocon pore into the target cell membrane, which serves as a conduit for the passage of effector proteins. Therefore the initial interaction between T3SS-bearing bacteria and host cells is the critical step in the deployment of the protein secretion machine, yet this process remains poorly understood. Here, we use high-throughput cryo-electron tomography (cryo-ET) to visualize the T3SS-mediated Salmonella-host cell interface. Our analysis reveals the intact translocon at an unprecedented level of resolution, its deployment in the host cell membrane, and the establishment of an intimate association between the bacteria and the target cells, which is essential for effector translocation. Our studies provide critical data supporting the long postulated direct injection model for effector translocation.
Comparative Analysis of Two Cryogenic Force Balance Calibration Systems

Cryogenic wind-tunnel facilities face unique challenges in the calibration and operation of various measurement systems and instrumentation. Instruments that are subjected to the cryogenic conditions of the test plenum require careful design and calibration procedures to maintain instrument performance. NASA’s National Transonic Facility (NTF) and the European Transonic Windtunnel (ETW) are two cryogenic wind-tunnel facilities, each with the ability to calibrate force measurement systems (FMS) at cryogenic conditions. These facilities have different methodologies and processes for calibrating these systems. This paper discusses differences in the methodologies and processes and compares the results of two separate cryogenic calibrations of the NTF-118A force balance that were completed at both wind-tunnel facilities.
Triad-based Neural Network for Coreference Resolution

We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.
PDF-1 neuropeptide signaling regulates sexually dimorphic gene expression in shared sensory neurons of C. elegans

Sexually dimorphic behaviors are a feature common to species across the animal kingdom, however how such behaviors are generated from mostly sex-shared nervous systems is not well understood. Building on our previous work which described the sexually dimorphic expression of a neuroendocrine ligand, DAF-7, and its role in behavioral decision-making in C. elegans (Hilbert and Kim, 2017), we show here that sex-specific expression of daf-7 is regulated by another neuroendocrine ligand, Pigment Dispersing Factor (PDF-1), which has previously been implicated in regulating male-specific behavior (Barrios et al., 2012). Our analysis revealed that PDF-1 signaling acts sex- and cell-specifically in the ASJ neurons to regulate the expression of daf-7, and we show that differences in PDFR-1 receptor activity account for the sex-specific effects of this pathway. Our data suggest that modulation of the sex-shared nervous system by a cascade of neuroendocrine signals can shape sexually dimorphic behaviors.
Revealed Preference Dimension via Matrix Sign Rank

Given a data-set of consumer behaviour, the Revealed Preference Graph succinctly encodes inferred relative preferences between observed outcomes as a directed graph. Not all graphs can be constructed as revealed preference graphs when the market dimension is fixed. This paper solves the open problem of determining exactly which graphs are attainable as revealed preference graphs in d-dimensional markets. This is achieved via an exact characterization which closely ties the feasibility of the graph to the Matrix Sign Rank of its signed adjacency matrix. The paper also shows that when the preference relations form a partially ordered set with order-dimension k, the graph is attainable as a revealed preference graph in a k-dimensional market.
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation

We propose a novel GAN-based framework for detecting shadows in images, in which a shadow detection network (D-Net) is trained together with a shadow attenuation network (A-Net) that generates adversarial training examples. The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net’s shadow predictions. Hence, it is effectively augmenting the training data for D-Net with hard-to-predict cases. The D-Net is trained to predict shadows in both original images and generated images from the A-Net. Our experimental results show that the additional training data from A-Net significantly improves the shadow detection accuracy of D-Net. Our method outperforms the state-of-the-art methods on the most challenging shadow detection benchmark (SBU) and also obtains state-of-the-art results on a cross-dataset task, testing on UCF. Furthermore, the proposed method achieves accurate real-time shadow detection at 45 frames per second.
Fundamental Analysis Redux

ABSTRACT In their classic text Security Analysis, Graham and Dodd (1934) warn investors against sole reliance on a few quantitative factors in investment decisions. Instead, they recommend that inv...
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention

This paper is a contribution towards interpretability of the deep learning models in different applications of time-series. We propose a temporal attention layer that is capable of selecting the relevant information to perform various tasks, including data completion, key-frame detection and classification. The method uses the whole input sequence to calculate an attention value for each time step. This results in more focused attention values and more plausible visualisation than previous methods. We apply the proposed method to three different tasks. Experimental results show that the proposed network produces comparable results to a state of the art. In addition, the network provides better interpretability of the decision, that is, it generates more significant attention weight to related frames compared to similar techniques attempted in the past.
On generalized symmetries and structure of modular categories

Pursuing a generalization of group symmetries of modular categories to category symmetries in topological phases of matter, we study linear Hopf monads. The main goal is a generalization of extension and gauging group symmetries to category symmetries of modular categories, which include also categorical Hopf algebras as special cases. As an application, we propose an analogue of the classification of finite simple groups to modular categories, where we define simple modular categories as the prime ones without any nontrivial normal algebras.
Modeling the interactive effects of spruce beetle infestation and climate on subalpine vegetation

In the subalpine zone of the Rocky Mountains, climate change is predicted to result in an increase in the frequency and severity of spruce beetle outbreaks. Climate change itself may affect vegetation, potentially leading to changes in species composition. The direct and indirect effects of climate and disturbances on forest composition, biomass, and dynamics open the possibility for non-linear ecosystem responses. Modeling studies allow for the study of the interaction of these effects and their impact on the forest system. University of Virginia Forest Model Enhanced (UVAFME), an individual-based gap model that simulates forest dynamics and characteristics, is updated with a spruce beetle subroutine that calculates the probability for beetle infestation and potential mortalityof each tree on a plot. The updated model is then run with multiple scenarios that combine beetle infestation with current or altered climate at sites across the southern Rocky Mountains. Results show that spruce beetle infestations acted to facilitate competition with invading lower-elevation species, resulting in an increase in the biomass of historically lower elevation species and a further decline in Engelmann spruce biomass than occurred with solely bark beetle disturbance or solely climate change. We also found an initial enhancing effect between spruce beetle infestation and climate change; however, by the end of 100 yr of climate change and potential beetle infestation, climate had a dampening effect on spruce beetle infestation, through loss of host trees. These results are an important step in understanding the possible futures for vegetation of the Rocky Mountains as well as for spruce forests across the western United States and Canada.
Value of high-quality logistics: Evidence from a clash between SF express and alibaba

Consumers regard product delivery as an important service component that influences their shopping decisions on online retail platforms. Delivering products to customers in a timely and reliable ma...
Learning to Learn Semantic Parsers from Natural Language Supervision

As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users’ corrections such as “no, what I really meant was before his job, not after”, by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.
Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.

The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an adaptive delayed acceptance Metropolis–Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare b...
Tariffs and quotas in world trade: A unified variational inequality framework

Abstract In this paper, we develop a unified variational inequality framework in the context of spatial price network equilibrium problems that captures world trade policies in the form of tariff rate quotas, which are two-tiered tariffs, and that have been applied in practice to numerous commodities. The spatial price network equilibrium model allows for multiple supply markets and multiple demand markets in different countries as well as multiple transportation routes. The model is qualitatively analyzed and also related to models with trade policies such as ad valorem tariffs and strict quotas. A case study on the dairy industry focusing on the United States and France is presented to illustrate the modeling and computational framework with impacts of stricter quotas, higher over quota tariffs, as well as additional transportation routes assessed. The numerical examples reveal that although domestic producers can gain under tariff rate quotas, consumers may experience higher prices. Given the relevance of tariffs to global trade in the world today and discussions concerning the impacts, the framework constructed in this paper is especially timely.
Optogenetic and transcriptomic interrogation of enhanced muscle function in the paralyzed mouse whisker pad.

Optogenetic activation of muscle can be used to noninvasively induce movements and probe muscle function. We used this technique in mice to investigate changes in whisker movements following facial...
Thermal Resistance by Transition Between Collective and Non-Collective Phonon Flows in Graphitic Materials

Phonons in graphitic materials exhibit strong normal scattering (N-scattering) compared to umklapp scattering (U-scattering). The strong N-scattering cause collective phonon flow, unlike the relati...
Learning protein sequence embeddings using information from structure

Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.
Land use restrictions, misallocation in agriculture, and aggregate productivity in Vietnam

We evaluate the effects of restricted land use rights on aggregate productivity using micro-level data within a quantitative model. In particular, we exploit the Rice Land Designation Policy in Vietnam, which forces farmers to produce rice on almost 45% of plots of land. The policy provides a natural setting for investigating the aggregate effects of land use misallocation. We quantify the impacts of this system by formulating a two-sector model featuring production and occupation choices. We also use digitized versions of Vietnam’s Local Land Use Atlas and Global Agro-Ecological Zones database to construct a micro-spatial dataset that shapes important features of the model and allows us to compare the restricted against a counterfactual efficient allocation. The main findings suggest that eliminating all land use restrictions leads to 10.6% gain in agricultural total factor productivity and 4.36% increase in real GDP per capita. While misallocation in agriculture has been studied extensively, our research highlights a novel source of misallocation that is prevalent in other countries such as China, Myanmar, Uzbekistan, among others.
Reliable Routing of Road-Rail Intermodal Freight under Uncertainty

Transportation infrastructures, particularly those supporting intermodal freight, are vulnerable to natural disasters and man-made disasters that could lead to severe service disruptions. These disruptions can drastically degrade the capacity of a transportation mode and consequently have adverse impacts on intermodal freight transport and freight supply chain. To address service disruption, this paper develops a model to reliably route freight in a road-rail intermodal network. Specifically, the model seeks to provide the optimal route via road segments (highway links), rail segments (rail lines), and intermodal terminals for freight when the network is subject to capacity uncertainties. To ensure reliability, the model plans for reduced network link, node, and intermodal terminal capacity. A major contribution of this work is that a framework is provided to allow decision makers to determine the amount of capacity reduction to consider in planning routes to obtain a user-specified reliability level. The proposed methodology is demonstrated using a real-world intermodal network in the Gulf Coast, Southeastern, and Mid-Atlantic regions of the United States. It is found that the total system cost increases with the level of capacity uncertainty and with increased confidence levels for disruptions at links, nodes, and intermodal terminals.
Tracking the origin of two genetic components associated with transposable element bursts in domesticated rice

Transposable elements (TEs) shape genome evolution through periodic bursts of amplification. In this study prior knowledge of the mPing/Ping/Pong TE family is exploited to track their copy numbers and distribution in genome sequences from 3,000 accessions of domesticated Oryza sativa (rice) and the wild progenitor Oryza rufipogon. We find that mPing bursts are restricted to recent domestication and is likely due to the accumulation of two TE components, Ping16A and Ping16A_Stow, that appear to be critical for mPing hyperactivity. Ping16A is a variant of the autonomous element with reduced activity as shown in a yeast transposition assay. Transposition of Ping16A into a Stowaway element generated Ping16A_Stow, the only Ping locus shared by all bursting accessions, and shown here to correlate with high mPing copies. Finally, we show that sustained activity of the mPing/Ping family in domesticated rice produced the components necessary for mPing bursts, not the loss of epigenetic regulation.
Solar Wind-Magnetosphere Interaction as Simulated by a 3D, EM Particle Code

The results of simulating the solar wind-magnetosphere interaction with a three dimensional, electromagnetic (EM) particle code are presented. Hitherto such global simulations were done with magnetohydrodynamic (MHD) codes while lower dimensional particle or hybrid codes served to account for microscopic processes and such transport parameters as have to be introduced ad hoc in MHD. The kinetic model combines macroscopic and microscopic tasks. It relies only on the Maxwell curl equations and the Lorentz equation for particles. The preliminary results are for an unmagnetized solar wind plasma streaming past a dipolar magnetic field. The results show the formation of a bow shock and a magnetotail, the penetration of energetic particles into cusp and radiation belt regions, and dawn to dusk asymmetries.