Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating degree-day factors from MODIS for snowmelt runoff modeling

Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDF S ) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDF S values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDF S estimated from snow data with those using spatially uniform DDF S calibrated on runoff. The runoff performances using estimated DDF S are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km 2 . Approximately 70% of the basin is covered by snow in the early spring season.
Sixteen years of lead poisoning in eagles, 1980-95: An epizootiologic view

A•STRACT. --A 16-yr (1980-95) retrospective study was conducted to assess differences in the prevalence of lead poisoning in Bald (Haliaeetus leucocephalus) and Golden (Aquila chrysaetos) Eagles admitted to The Raptor Center at the University of Minnesota. These years encompass the period before and after federal legislation was enacted restricting the use of lead shot for hunting waterfowl on federal lands (1991). Of 654 eagle admissions reviewed, 138 cases of lead-poisoned eagles were evaluated for the following: recovery location, blood lead concentration, month of admission, radiographic evidence of lead in the ventriculus and primary cause of admission. The prevalence of lead poisoning in eagles did not change after 1991, but mean blood concentrations of lead in the same population decreased. These findings call into question current theories regarding the sources of lead for eagles and the actual mechanisms by which eagles are poisoned. Lead poisoning is a continuing problem both regionally and internationally, and many variables related to this toxicity have yet to be conclusively defined.
Laminar mixing of a compressible fluid

A theoretical investigation of the velocity profiles for laminar mixing of a high-velocity stream with a region of fluid at rest has been made assuming that the Prandtl number is unity. A method which involves only quadratures is presented for calculating the velocity profile in the mixing layer for an arbitrary value of the free-stream Mach number. Detailed velocity profiles have been calculated for free-stream Mach numbers of 0, 1, 2, 3, and 5. For each Mach number, velocity profiles are presented for both a linear and a 0.76-power variation of viscosity with absolute temperature. The calculations for a linear variation are much simpler than those for a 0.76-power variation. It is shown that by selecting the constant of proportionality in the liner approximation such that it gives the correct value for the viscosity in the high-temperature part of the mixing layer, the resulting velocity profiles are in excellent agreement with those calculated by a 0.76-power variation.
When Marketing Strategy First Meets Wall Street: Marketing Spendings and Firms’ Initial Public Offerings

This article examines the role of marketing in the context of initial public offerings (IPOs), a neglected issue in the extant literature. The results from a large-scale, cross-industry study indicate that firms’ pre-IPO marketing spendings help reduce IPO underpricing and boost IPO trading in the stock market. The econometric models also suggest that these effects are heterogeneous; that is, they are more salient for firms with higher cost reduction efficiency and in markets with a smaller number of historical IPOs. With regard to theory, this research ushers in a greenfield of IPOs, helping build more powerful theories of market-based assets and customer equity. With regard to practice, it builds the case for not cutting marketing before an IPO. Prudent investors may be better able to pick “star” IPOs if they can track pre-IPO marketing spendings and model firm cost reduction efficiency simultaneously. Overall, this article offers fresh implications for the marketing–finance interface, uncovering brand-new IPO-based reasons that marketing can help create shareholder value.
Public Transfers and Domestic Violence: The Roles of Private Information and Spousal Control

Existing economic theories of the family suggest that public transfer programs in which funds are targeted to women, by improving women’s bargaining position in the household, may decrease the incidence of spousal abuse. We study this prediction empirically using data from a unique survey in Mexico to examine the impact of the Oportunidades conditional cash transfer program on spousal abuse rates and threats of violence. We find that although women in beneficiary households are 33 percent less likely to be victims of physical abuse than women in comparable non-beneficiary households, they are more likely to receive violent threats with no associated physical abuse. We re-interpret a model of decision-makers’ interactions with asymmetric information in the male partners’ gains to marriage to document how increases in female partners’ socioeconomic opportunities can lead to an increase in husbands’ use of violent threats with no associated physical abuse – predictions consistent with the empirical evidence.
Postoperative Resolution of Magnetic Resonance Imaging Signal Intensity Changes and the Associated Impact on Outcomes in Degenerative Cervical Myelopathy: Analysis of a Global Cohort of Patients

Study Design. Subanalysis of the prospective AOSpine CSM North America and International studies. Objective. To describe the postoperative changes in magnetic resonance imaging (MRI) spinal cord signal intensity in degenerative cervical myelopathy (DCM) patients and to investigate the impact of its postoperative resolution on clinical outcomes. Summary of Background Data. When examining the spinal cord, hyperintensity found in MRI T2-weighted images and hypointensity in T1-weighted images are known to correlate with preoperative severity of DCM and to predict postoperative neurological recovery. However, the clinical importance of these signal intensity changes in postoperative images has not been established. Methods. Among 757 surgical DCM patients enrolled in two prospective multicenter studies, postoperative MRI images obtained between 6 and 24 months after the operation were examined with a focus on T2 hyper- and T1 hypointensity in the spinal cord. The 2-year postoperative Nurick grade, modified Japanese Orthopaedic Association score and modified Japanese Orthopaedic Association recovery rate (RR) were analyzed between patients with or without resolution of signal intensity changes. Results. A total of 167 patients with preoperative T2 hyperintensity were included with complete postoperative MRI images. Of these patients, 11% showed resolution of signal intensity changes, 70% retained T2 hyperintensity only, and 19% showed both T2 hyper- and T1 hypointensity postoperatively. There was a stepwise trend toward worse postoperative outcomes, with the no signal intensity change group showing the best outcome and the T1 hypointensity group showing the worst (mean RR: 72% vs. 51% vs. 36%, P = 0.02). Patients who exhibited resolution of T2 hyperintensity showed better outcomes than those who retained it (RR: 72% vs. 47%, P = 0.04), but the resolution of T1 hypointensity was not associated with improved outcomes (RR: 38% vs. 26%, P = 0.36). Conclusion. Postoperative resolution of T2 hyperintensity in patients with DCM was associated with the best clinical outcomes, whereas those with T1 hypointensity showed the worst. Level of Evidence: 3
Global-and-local attention networks for visual recognition

State-of-the-art deep convolutional networks (DCNs) such as squeeze-and- excitation (SE) residual networks implement a form of attention, also known as contextual guidance, which is derived from global image features. Here, we explore a complementary form of attention, known as visual saliency, which is derived from local image features. We extend the SE module with a novel global-and-local attention (GALA) module which combines both forms of attention -- resulting in state-of-the-art accuracy on ILSVRC. We further describe ClickMe.ai, a large-scale online experiment designed for human participants to identify diagnostic image regions to co-train a GALA network. Adding humans-in-the-loop is shown to significantly improve network accuracy, while also yielding visual features that are more interpretable and more similar to those used by human observers.
Integrated Effects of Rhizobial Inoculum and Inorganic Fertilizers on Wheat Yield and Yield Components

An experiment was conducted in pots under natural condition with two factor factorial completely randomized design (CRD) (CRD) to investigate the integrated effect of rhizobia inoculums and inorganic fertilizers on growth and yield of wheat crop at The University of Agriculture Peshawar, during 2012-2013. The experiment was comprised of four inoculums (no, lentil, peas and chickpeas) and two NPK levels (recommended 120:90:60 kg·ha-1 & 20% less of recommended). It was observed that inoculation of wheat by rhizobia significantly increased tillers per plant by a maximum of 42%, plant height by 13%, grain per spike by 16%, 100 grain weight by 10%, biological and grain yield by 10% over un-inoculated (control) treatment. Among inoculums, peas inoculum was found to be the most efficient for all traits except plant height where chickpea inoculum performed better. Similarly recommended NPK significantly increased tillers per plant by 33%, plant height by 19%, grain per spike by 9%, 100 grain weight by 10%, biological yield by 8% and grain yield by 10% compared with 20% less of recommended NPK. Interactive effect of inoculum x NPK was significant for tillers per plant, grain per spike, grain yield and non-significant for plant height, 100 grain weight and biological yield. However, it was evident from the results that inoculation improved all traits both under recommended and 20% less of recommended NPK. The persistent good performance of peas and lentil rhizobial inoculation in wheat growth exhibited that this could be used as a plant growth promoting rhizobacteria for wheat and other cereal crops in prevailing soil and climatic conditions.
Both the intratumoral immune and microbial microenvironment are linked to recurrence in human colon cancer: results from a prospective, multicenter nodal ultrastaging trial

Colon cancer (CC) is the third most common cancer diagnosed in the United States and the incidence has been rising among young adults. We and others have shown a relationship between the immune infiltrate and prognosis, with improved disease-free survival (DFS) being associated with a higher expression of CD8+ T cells. We hypothesized that a microbial signature might be associated with intratumoral immune cells as well as DFS. We found that the relative abundance of one Operational Taxonomic Unit (OTU), OTU_104, was significantly associated with recurrence even after applying false discovery correction (HR 1.21, CI 1.08 to 1.36). The final multivariable model showed that DFS was influenced by three parameters: N-stage, CD8+ labeling, as well as this OTU_104 belonging to the order Clostridiales. Not only were CD8+ labeling and OTU_104 significant contributors in the final DFS model, but they were also inversely correlated to each other (p=0.022). Interestingly, CD8+ was also significantly associated with the microbiota composition in the tumor: CD8+ T cells was inversely correlated with alpha diversity (p=0.027) and significantly associated with the beta diversity. This study is the first to demonstrate an association among the intratumoral microbiome, CD8+ T cells, and recurrence in CC. An increased relative abundance of a specific OTU_104 was inversely associated with CD8+ T cells and directly associated with CC recurrence. The link between this microbe, CD8+ T cells, and DFS has not been previously shown.
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control. While alpha waves (8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed by Fourier analysis or wavelet transforms, there is an evolving debate that electromagnetic neural signals are composed of more complex waveforms that cannot be analyzed by linear filters and traditional signal representations. In this paper, we propose to learn dedicated representations of such recordings using a multivariate convolutional sparse coding (CSC) algorithm. Applied to electroencephalography (EEG) or magnetoencephalography (MEG) data, this method is able to learn not only prototypical temporal waveforms, but also associated spatial patterns so their origin can be localized in the brain. Our algorithm is based on alternated minimization and a greedy coordinate descent solver that leads to state-of-the-art running time on long time series. To demonstrate the implications of this method, we apply it to MEG data and show that it is able to recover biological artifacts. More remarkably, our approach also reveals the presence of non-sinusoidal mu-shaped patterns, along with their topographic maps related to the somatosensory cortex.
Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network

Rice has been one of the staple foods that contribute significantly to human food supplies. Numerous rice varieties have been cultivated, imported, and exported worldwide. Different rice varieties could be mixed during rice production and trading. Rice impurities could damage the trust between rice importers and exporters, calling for the need to develop a rice variety inspection system. In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN). The proposed method uses a hyperspectral imaging system to simultaneously acquire complementary spatial and spectral information of rice seeds. The rice varieties are then determined from the acquired spatio-spectral data using a deep CNN. As opposed to several existing rice variety classification methods that require hand-engineered features, the proposed method automatically extracts features from the raw sensor data. As demonstrated using two types of rice datasets, the proposed method achieved up to 8% relative improvement in the classification accuracy compared to the commonly used classification methods based on support vector machines.
Playing hard exploration games by watching YouTube

Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator. However, these demonstrations are typically collected under artificial conditions, i.e. with access to the agent's exact environment setup and the demonstrator's action and reward trajectories. Here we propose a two-stage method that overcomes these limitations by relying on noisy, unaligned footage without access to such data. First, we learn to map unaligned videos from multiple sources to a common representation using self-supervised objectives constructed over both time and modality (i.e. vision and sound). Second, we embed a single YouTube video in this representation to construct a reward function that encourages an agent to imitate human gameplay. This method of one-shot imitation allows our agent to convincingly exceed human-level performance on the infamously hard exploration games Montezuma's Revenge, Pitfall! and Private Eye for the first time, even if the agent is not presented with any environment rewards.
Bi-Directional Neural Machine Translation with Synthetic Parallel Data

Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.
Nummularic acid, a triterpenoid, from the medicinal plant Fraxinus xanthoxyloides, induces energy crisis to suppress growth of prostate cancer cells

We recently identified and characterized nummularic acid (NA) as a major chemical constituent of Fraxinus xanthoxyloides, a medicinal plant used for over hundred years in traditional medicine. In this study, we describe its potential anti‐cancer activity using prostate cancer (PCa) cells as a model. We found that NA treatment (5‐60 μM) significantly reduced the proliferation and colony formation capabilities of PCa DU145 and C4‐2 cells in a time and dose dependent manner, reduced the migratory and invasive properties and increased apoptotic cell population. Mechanistically, we found that NA treatment to PCa cells resulted in a sustained activation of adenosine monophosphate‐activated protein kinase (AMPK). NA simultaneously increased acetyl CoA carboxylase phosphorylation and decreased pS6 phosphorylation, the two major substrates of AMPK. Further, NA treatment significantly elevated the cellular ADP/ATP ratio and altered glycolytic rate. We further observed a reversible decrease in oxygen consumption rate in NA treated cells when compared to the control. Finally, we performed global untargeted metabolomics which showed that NA treatment alters PCa cell metabolism at multiple sites including glycolysis, tricarboxylic acid, and glutamine metabolism which supported our observation of a possible AMPK activation. In summary, we report NA as a novel small molecule activator of AMPK that alters cellular metabolism to induce energy crisis and ultimately cancer cell death. Because of its unique mechanism NA could be potentially applicable against other cancer types.
End-to-End Speech-Driven Facial Animation with Temporal GANs

Speech-driven facial animation is the process which uses speech signals to automatically synthesize a talking character. The majority of work in this domain creates a mapping from audio features to visual features. This often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present a system for generating videos of a talking head, using a still image of a person and an audio clip containing speech, that doesn't rely on any handcrafted intermediate features. To the best of our knowledge, this is the first method capable of generating subject independent realistic videos directly from raw audio. Our method can generate videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. We achieve this by using a temporal GAN with 2 discriminators, which are capable of capturing different aspects of the video. The effect of each component in our system is quantified through an ablation study. The generated videos are evaluated based on their sharpness, reconstruction quality, and lip-reading accuracy. Finally, a user study is conducted, confirming that temporal GANs lead to more natural sequences than a static GAN-based approach.
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception

While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial. However, while CNNs capture key properties of the average responses of cortical neurons, they fail to explain other properties of these neurons. For one, CNNs typically require large quantities of labeled input data for training. Our own brains, in contrast, rarely have access to this kind of supervision, so to the extent that representations are similar between CNNs and brains, this similarity must arise via different training paths. In addition, neurons in visual cortex produce complex time-varying responses even to static inputs, and they dynamically tune themselves to temporal regularities in the visual environment. We argue that these differences are clues to fundamental differences between the computations performed in the brain and in deep networks. To begin to close the gap, here we study the emergent properties of a previously-described recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single unit response dynamics to complex perceptual motion illusions. These results suggest potentially deep connections between recurrent predictive neural network models and the brain, providing new leads that can enrich both fields.
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification

We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC). The algorithm operates in $K$ stages of parallel function evaluations and trains a cascade of binary classifiers to iteratively cull the undesirable regions of the search space. SHAC is easy to implement, requires no tuning of its own configuration parameters, is invariant to the scale of the objective function and can be built using any choice of binary classifier. We adopt tree-based classifiers within SHAC and achieve competitive performance against several strong baselines for optimizing synthetic functions, hyperparameters and architectures.