Miracle-dz/UniFAR-FTUs huggingface dataset:
Water resources estimation model based on remote sensing evapotranspiration

There are many problems for the current water resource estimation methods. For the traditional water resources assessment method, the runoff reduction cannot be accurately estimated, because the unmeasured terms of runoff reduction are either neglected or statistically estimated according to the local population and water use ration. Also, the hydrogeological parameters used to calculate the water replenishment to aquifer are empirical. Furthermore, the traditional water resources assessment method is labor consuming and fund consuming. For the hydrological model, model processes are complicated, and parameters should be calibrated with a lot of observation data. In order to solve the above-mentioned problems, this paper proposes an innovative water resources estimation model based on remote sensing evapotranspiration (ET). There, the water resources are defined as the difference between precipitation and uncontrollable ET. The uncontrollable ET is the natural ET from natural surface and doesn't include the ET from water consumption of humans. By combining the remote sensing ET and land use according to the natural characteristic of land uses, the uncontrollable ET is obtained. While the cropland uncontrollable ET is obtained by extracting the soil evaporation using satellite based Normalized Difference Vegetation Index (NDVI). A case study result shows that calculated water resources are relatively high correlated with that of obtaining from water resources reports. And the deviation of the calculated water resources can be eliminated by improving the accuracy of the precipitation interpolation and land use classification.
Discovering auditing criteria for the going-concern disclaimer

Accounting standards require auditors to attach an explanatory paragraph to their audit report if they have substantial doubt about an entity's ability to continue as a going-concern. In the paragraph, auditors provide the rationale for the opinion. Given the corporation's financial ratios, machine learning can quantify this rationale. This study induces decision rules from cases of financially distressed companies for the period of economic expansion, in the USA, from 1991 to 2000. The induced rules for the opinion agree with two factors frequently cited in the corresponding audit reports: stockholders' deficiency and default. The values of only two rules can correctly identify 80% of the decisions. The induced trees yield specific rules corresponding to an expert system for the going-concern decision.
Deep reinforcement learning for tensegrity robot locomotion

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and supplementary material is available from http://rll.berkeley. edu/drl_tensegrity.
Does diversity damage corporate value? Measuring stock price reactions to a diversity award

Abstract Corporate decision-makers have increasingly adopted an approach to firm governance that places share value above other concerns, including meeting the needs of employees. Decision-makers' concerns about shareholder interests may potentially impede internal commitments to implementing diversity-friendly practices and policies. This study analyses how recognition for a company's diversity efforts impacts corporate share price. We rely on data of share price fluctuation following the receipt of Fortune Magazine's ‘Best Companies for Minorities’ Award. We find that companies recognized for their diversity efforts are penalized with a significant decline in share price, though this negative effect is mediated by the ethnic composition of the company's workforce.
Role of Trust in Evolution of Scientific Collaboration Networks

Recent studies indicate that collaborations among researchers reflect a social relation among them. The strength of the relation is often determined by the number of publications being made by the collaborating researchers. Thus this relation among the collaborators can be represented by a weighted network, where the nodes represent the authors and the links weights are determined by the strength of the relation. In this paper, we study the evolution of such weighted networks based on the mutual trust among the authors (that we term as link trust) as well as the global trust of the authors in the network (that we term as node trust). We have identified various metrices to measure these trusts. Based on these measures we have analyzed the role of both node as well as link trust in the evolution of networks. Our observations are based on a publication data set in the area of computer science. We have also proposed suitable analytical models that closely mimics the evolution of these networks based on these trust characteristics. Our results indicate that these trust parameters play significant, yet different roles in evolution of those networks.
The Rab5 Effector Rabankyrin-5 Regulates and Coordinates Different Endocytic Mechanisms

The small GTPase Rab5 is a key regulator of clathrin-mediated endocytosis. On early endosomes, within a spatially restricted domain enriched in phosphatydilinositol-3-phosphate [PI(3)P], Rab5 coordinates a complex network of effectors that functionally cooperate in membrane tethering, fusion, and organelle motility. Here we discovered a novel PI(3)P-binding Rab5 effector, Rabankyrin-5, which localises to early endosomes and stimulates their fusion activity. In addition to early endosomes, however, Rabankyrin-5 localises to large vacuolar structures that correspond to macropinosomes in epithelial cells and fibroblasts. Overexpression of Rabankyrin-5 increases the number of macropinosomes and stimulates fluid-phase uptake, whereas its downregulation inhibits these processes. In polarised epithelial cells, this function is primarily restricted to the apical membrane. Rabankyrin-5 localises to large pinocytic structures underneath the apical surface of kidney proximal tubule cells, and its overexpression in polarised Madin-Darby canine kidney cells stimulates apical but not basolateral, non-clathrin-mediated pinocytosis. In demonstrating a regulatory role in endosome fusion and (macro)pinocytosis, our studies suggest that Rab5 regulates and coordinates different endocytic mechanisms through its effector Rabankyrin-5. Furthermore, its active role in apical pinocytosis in epithelial cells suggests an important function of Rabankyrin-5 in the physiology of polarised cells.
Tracking people within groups with RGB-D data

This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation.
Preparation of Coenzyme Q10 nanostructured lipid carriers for epidermal targeting with high-pressure microfluidics technique

Objective: The objective of this work was to prepare coenzyme Q10 loaded nanostructured lipid carriers (Q10-NLC) and evaluate its epidermal targeting effect. Methods: Q10-NLC was prepared by high-pressure microfluidics technique. Formulations and preparation parameters were optimized with response surface design. Q10-NLC was characterized by PCS, TEM, DSC and PXRD. The penetration of Q10 from the Q10-NLC formulations through skins and into skins were evaluated in vitro using Franz diffusion cells fitted with SD rat skins. In vitro release, long-term stability and light stability were also evaluated. Results: The results showed that the concentration of solid lipid and emulsifier in formulation had a significant influence on particle size. The optimized preparation parameters were magnetic stirring for 20 min, high stirring at 8000 rpm for 1 min and high-pressure microfluidics at 1200 bar for three cycles. The size of Q10-NLC prepared by optimized formulation and parameters was (151.7 ± 2.31) nm, polydispersity (PDI) 0.144, ζ potential was (−44.1 ± 1.68) mV, drug loading 2.51%, encapsulation efficiency 100%. In vitro release study, Q10-NLC showed fast release during the first 3 hours and prolonged release afterwards. In vitro skin permeation study, the accumulative uptake of Q10 in epidermal of Q10-NLC was 10.11 times over Q10 emulsion. After exposure to day light for 24 hours, the amount of Q10 in Q10-NLC decreased only 5.59%, while in Q10 emulsion decreased 24.61% and Q10-ethanol solution 49.74%. Conclusion: Q10-NLC exhibited a significant epidermal targeting effect, which was proved to be a promising carrier for topical delivery of Q10.
Program Evaluation as an Evolutionary Process

This article discusses ongoing program evaluation as an evolutionary process in which the form of evaluation changes in response to the questions to be addressed. The concept implies that as programs evolve over time, so should the evaluation designs; furthermore this evolutionary process can result in different evaluation approaches being used in subsequent cycles of investigation. The concept of evaluation as an evolutionary process is illustrated with two examplesfrom a large school district in Florida. One evaluation dealt with a systemwide reading laboratory program at the secondary level; the other was concerned with an alternative educational program for sixth-grade students at a sample of schools. In both examples the form of evaluation used to gather information about ongoing programs evolved over several years of operation in response to evaluation findings and newly identified concerns.
Translational Research in Acute Lung Injury and Pulmonary Fibrosis Angiotensin-( 1 – 7 ) / mas inhibits apoptosis in alveolar epithelial cells through upregulation of MAP kinase phosphatase-2

Gopallawa I, Uhal BD. Angiotensin-(1–7)/mas inhibits apoptosis in alveolar epithelial cells through upregulation of MAP kinase phosphatase-2. Am J Physiol Lung Cell Mol Physiol 310: L240–L248, 2016. First published December 4, 2015; doi:10.1152/ajplung.00187. 2015. —Earlier work from this laboratory showed that autocrine generation of angiotensin II and c-Jun-NH2-terminal kinase phosphorylation (p-JNK) are both required events in alveolar epithelial cell (AEC) apoptosis. Although earlier data showed that angiotensin-(1–7) [ANG-(1–7)] protects against AEC apoptosis, the pathways by which ANG-(1–7)/mas activation prevent JNK phosphorylation and apoptosis are poorly understood. Therefore, in the current study, it was theorized that ANG-(1–7) activates a mitogen-activated protein kinase phosphatase (MKP) and thereby reduces JNK phosphorylation to inhibit apoptosis and promote cell survival. This hypothesis was evaluated in the human A549 and mouse MLE12 AEC lines and primary cultures of human AECs. Cells were transfected with small-interfering RNAs, antisense oligonucleotides, or inhibitors specific for MKP-2 or mas, and were then assayed for phosphoJNK, caspase-9, loss of mitochondrial membrane potential, and nuclear fragmentation. Silencing of MKP-2 significantly prevented the blockade of all apoptotic markers by ANG-(1–7). Knockdown or blockade of mas receptor by antisense oligonucleotides or by the receptor antagonist A779, respectively, caused significant decreases in MKP-2, and simultaneously increased the apoptotic markers of caspase-9 activation and nuclear fragmentation. These data show that the ANG-(1–7)/mas pathway constitutively prevents JNK phosphorylation and apoptosis of AECs by maintaining activation of the JNK-selective phosphatase MKP-2, and further demonstrate the critical role of the ANG-(1–7) receptor mas in AEC survival.
Image Registration for Multi-exposure High Dynamic Range Image Acquisition

We present a fully automatic method for eliminating misalignments between a sequence of hand-held photographs taken at different exposures. The key component of the technique is the SIFT method that is employed to search for key-points (or feature-points) in consecutive images. The key-points are used to ﬁnd matrices, that transform a set of images to a single coordinate system and eliminate any global misalignments (including general planar homography). We employ this technique to capture high dynamic range images from a set of photographs taken at different exposures, where misalignments can cause blurring and artifacts, and prevent achieving high quality HDR images. The proposed alignment technique works well for over-and under-exposed images and is not sensitive to an image content. We present our implementation of the technique and the results of tests made for variety of photographs.
Portfolio Performance Evaluation in a Modified Mean-Variance-Skewness Framework with Negative Data

The present study is an attempt toward evaluating the performance of portfolios using meanvariance-skewness model with negative data. Mean-variance non-linear framework and meanvariance-skewness non- linear framework had been proposed based on Data Envelopment Analysis, which the variance of the assets had been used as an input to the DEA and expected return and skewness were the output. Conventional DEA models assume non-negative values for inputs and outputs. However, we know that unlike return and skewness, variance is the only variable in the model that takes non-negative values. This paper focuses on the evaluation process of the portfolios in a mean-variance-skewness model with negative data. The problem consists of choosing an optimal set of assets in order to minimize the risk and maximize return and positive skewness. This method is illustrated by application in Iranian stock companies and extremely efficiencies are obtained via mean-variance-skewness non-linear framework with negative data for making the best portfolio. The finding could be used for constructing the best portfolio in stock companies, in various finance organization and public and private sector companies.
Computing Gap Free Pareto Front Approximations with Stochastic Search Algorithms

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of -dominance. Though bounds on the quality of the limit approximationwhich are entirely determined by the archiving strategy and the value of have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a A, is large. Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategymulti-objective continuation methodsby showing that the concept of -dominance can be integrated into this approach in a suitable way.
On activating human communications with pet-type robot AIBO

In this paper we describe effects of human interactions with a pet-type robot, especially with AIBO. First, we describe a design concept for AIBO based on how to increase its "lifelike" appearance. By introducing statistical results of marketing, and experiments involving human-robot interactions using AIBO, we show that this pet-type robot activates human emotions effectively. Furthermore, the experiments demonstrate that AIBO helps in human-human communication. We discuss the phenomena of interaction with AIBO, and attempt to explain why this happened.
The Voices of Vices

This article summarizes the results of a qualitative content analysis of historical literature on problem gamblers in America and England. Using the pathological gambling entry in the current Diagnostic and Statistical Manual of Mental Disorders as a framework, it is revealed that all of the diagnostic criteria have a long and documented history. The findings here suggest that in a number of important ways, researchers have merely medicalized their previous moral objections to gambling behavior. Clinical implications and future research implications are discussed.
Functional genomics identifies therapeutic targets for MYC-driven cancer

MYC oncogene family members are broadly implicated in human cancers, yet are considered “undruggable” as they encode transcription factors. MYC also carries out essential functions in proliferative tissues, suggesting that its inhibition could cause severe side effects. We elected to identify synthetic lethal interactions with c-MYC overexpression (MYC-SL) in a collection of ∼3,300 druggable genes, using high-throughput siRNA screening. Of 49 genes selected for follow-up, 48 were confirmed by independent retesting and approximately one-third selectively induced accumulation of DNA damage, consistent with enrichment in DNA-repair genes by functional annotation. In addition, genes involved in histone acetylation and transcriptional elongation, such as TRRAP and BRD4, were identified, indicating that the screen revealed known MYC-associated pathways. For in vivo validation we selected CSNK1e, a kinase whose expression correlated with MYCN amplification in neuroblastoma (an established MYC-driven cancer). Using RNAi and available small-molecule inhibitors, we confirmed that inhibition of CSNK1e halted growth of MYCN-amplified neuroblastoma xenografts. CSNK1e had previously been implicated in the regulation of developmental pathways and circadian rhythms, whereas our data provide a previously unknown link with oncogenic MYC. Furthermore, expression of CSNK1e correlated with c-MYC and its transcriptional signature in other human cancers, indicating potential broad therapeutic implications of targeting CSNK1e function. In summary, through a functional genomics approach, pathways essential in the context of oncogenic MYC but not to normal cells were identified, thus revealing a rich therapeutic space linked to a previously “undruggable” oncogene.
Genetic Differentiation among Populations of Cicerbita alpina (L.) Wallroth (Asteraceae) in the Western Alps

In this study we analyzed the genetic population structure of the hygrophilous tall-herb Cicerbita alpina in the western Alps because this group of mountain plants is underrepresented in the biogeographical literature. AFLP (amplified fragment length polymorphism) fingerprints of 40 samples were analyzed from four populations situated in a transect from the southwestern Alps to the eastern part of the western Alps and one population from the Black Forest outside the Alps. Two genetic groups can be distinguished. The first group (A) comprises the populations from the northern and eastern parts of the western Alps, and the second group (B) comprises the populations from the southwestern Alps and the Black Forest. Group A originates most likely from at least one refugium in the southern piedmont regions of the Alps. This result provides molecular evidence for a humid climate at the southern margin of the Alps during the Würm glaciation. Group B originates presumably from western or northern direction and we discuss two possible scenarios for the colonization of the Alps, i. e. (1) long-distance dispersal from southwestern refugia and (2) colonization from nearby refugia in the western and/or northern Alpine forelands. The study demonstrates that the target species harbours considerable genetic diversity, even on a regional scale, and therefore is a suitable model for phylogeographic research.
Remote estimation of chlorophyll-a concentrations in Taihu Lake during cyanobacterial algae bloom outbreak

Accurate remote assessment of chlorophyll-a (chla) concentration is particularly challenging in turbid waters, especially during the period of cyanobacterial algae bloom outbreak. In this study, we took Taihu Lake as an example, and presented a chl-a estimation method in the condition of cyanobacteria algae bloom outbreaking by using MODIS data. The main idea of this method is (1) to study the water reflectance spectra during the bloom outbreak, (2) to divide water into different areas based on the water reflectance spectra, and (3) to establish algorithms for chl-a estimation in different water areas. The results shows that the method greatly improved the accuracy of chl-a estimation during the cyanobacterial algae bloom outbreak in Taihu lake.
Predicting web searcher satisfaction with existing community-based answers

Community-based Question Answering (CQA) sites, such as Yahoo! Answers, Baidu Knows, Naver, and Quora, have been rapidly growing in popularity. The resulting archives of posted answers to questions, in Yahoo! Answers alone, already exceed in size 1 billion, and are aggressively indexed by web search engines. In fact, a large number of search engine users benefit from these archives, by finding existing answers that address their own queries. This scenario poses new challenges and opportunities for both search engines and CQA sites. To this end, we formulate a new problem of predicting the satisfaction of web searchers with CQA answers. We analyze a large number of web searches that result in a visit to a popular CQA site, and identify unique characteristics of searcher satisfaction in this setting, namely, the effects of query clarity, query-to-question match, and answer quality. We then propose and evaluate several approaches to predicting searcher satisfaction that exploit these characteristics. To the best of our knowledge, this is the first attempt to predict and validate the usefulness of CQA archives for external searchers, rather than for the original askers. Our results suggest promising directions for improving and exploiting community question answering services in pursuit of satisfying even more Web search queries.