Miracle-dz/UniFAR-FTUs huggingface dataset:
Organic synthesis on Mars by electrochemical reduction of CO2

Organic synthesis on Mars occurs by the electrochemical reduction of CO2, a reaction that is highly relevant for abiotic organic synthesis on early Earth. The sources and nature of organic carbon on Mars have been a subject of intense research. Steele et al. (2012) showed that 10 martian meteorites contain macromolecular carbon phases contained within pyroxene- and olivine-hosted melt inclusions. Here, we show that martian meteorites Tissint, Nakhla, and NWA 1950 have an inventory of organic carbon species associated with fluid-mineral reactions that are remarkably consistent with those detected by the Mars Science Laboratory (MSL) mission. We advance the hypothesis that interactions among spinel-group minerals, sulfides, and a brine enable the electrochemical reduction of aqueous CO2 to organic molecules. Although documented here in martian samples, a similar process likely occurs wherever igneous rocks containing spinel-group minerals and/or sulfides encounter brines.
Maximum entropy models for sequences: scaling up from tagging to translation

Maximum entropy approaches for sequences tagging and conditional random fields in particular have shown high potential in a variety of tasks. The effectiveness of these approaches is verified within this thesis using semantic tagging within natural language understanding as an example. Within this task, decent feature engineering and a tuning of the regularization parameter is sufficient to let conditional random fields be superior to a broad set of competing approaches including support vector machines, phrase-based translation, maximum entropy Markov models, dynamic Bayesian networks, and generatively trained probabilistic finite state transducers. Applying conditional random fields to other tasks in many cases calls for extensions to the original notation. For a multi-level semantic tagging in natural language understanding, constrained search is needed, whereas for grapheme-to-phoneme conversion, the support for a hidden segmentation and huge feature sets is required, and for statistical machine translation a solution for the large input and output vocabulary, even larger feature sets, and the hidden alignments have to be found. This thesis presents solutions to all these constraints. The conditional random fields are modeled with finite state transducers to support constraints on the search space. They are extended with hidden segmentation, elastic-net regularization, sparse-forward-backward, pruning in training, and intermediate classes in the output layer. Finally, we will combine all extensions to support statistical machine translation with conditional random fields. The best implementation for statistical machine translation is then based on a refined maximum expected Bleu objective using a similar feature notation and the same RPROP parameter estimation. It differs in a more efficient use of the phrase-based or hierarchical baseline with the help of n-best lists.
Principal Agent Theory in the Examination of Technical Specifications used in Government Information Technology Procurement

IT project requirements from non-technical users are captured and translated into technical specifications by the internal Information Technology department for IT goods and services acquisition. An aligned technical specification from users and other stakeholders is a critical success factor of large IT project implementations as they represent the blueprints of an IT project. in a qualitative study different Philippine government offices involved in some large IT procurement of goods and services, principal agent theory reveals that some gaps are created due to the adverse selection caused by moral hazards and hidden characteristics during pre-procurement, selection and evaluation and, implementation of an IT project. The study finds opportunism from vendors, IT personnel and political figures for long term profit and personal gain, and these negatively influence the quality and accuracy technical specification; thus resulting in major failures and huge losses.
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Background In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.
Social Media Word-of-Mouth in Crisis and Non-Crisis Context: Examining the Halo Effect of Corporate Reputation

Firms usually expect to benefit from social media technology through seeking social media positive word-of-mouth exposure. However, it turns out that firms suffers more from negative crisis communication among consumers on social media when corporate crises happen. This paper compares consumers’ decision progress of positive word-of-mouth and secondary crisis communication in non-crisis and crisis contexts and aims to give an insight into social media word-of-mouth management. Scenarios were used to design crisis and non-crisis contexts and the results showed that in non-crisis contexts, affective reputation contributes more to positive word-of-mouth than cognitive reputation. Meanwhile in crisis contexts, high cognitive reputation brings more secondary crisis communication than affective reputation. Furthermore, when consumers have high social outcome expectations towards social media use, they are less likely to engage in positive word-of-mouth in non-crisis context and are more likely to engage in secondary crisis communication in crisis contexts. about corporate reputation and social psychology research about social control. We also compare the effect of cognitive and affective components of corporate reputation on PWOM and SCC based on expectation-violation theory and affect investment theory. To summarize, we believe this study makes two significant contributions: First, this study promotes a better understanding of the essence of PWOM and SCC on social media by comparing consumers’ decision processes when engaging in PWOM in non-crisis times and SCC in crisis times. Our results will enhance firms’ understanding of better social media WOM management across crisis and non-crisis context. Second, our research broadens social media WOM literature by considering social media technology characteristics. This could improve existing understanding of social media WOM and help make social media WOM management more effective. investigating
O(log2 k / log log k)-approximation algorithm for directed Steiner tree: a tight quasi-polynomial-time algorithm

In the Directed Steiner Tree (DST) problem we are given an n-vertex directed edge-weighted graph, a root r , and a collection of k terminal nodes. Our goal is to find a minimum-cost subgraph that contains a directed path from r to every terminal. We present an O(log^2 k /log log k )-approximation algorithm for DST that runs in quasi-polynomial-time, i.e., in time n^polylog(k). By making standard complexity assumptions, we show the matching lower bound of Omega(log^2 k/loglogk) for the class of quasi-polynomial time algorithms, meaning that our approximation ratio is asymptotically the best possible. This is the first improvement on the DST problem since the classical quasi-polynomial-time O (log^3 k ) approximation algorithm by Charikar et al. [SODA’98J. Algorithms’99]. (The paper erroneously claims an O (log^2 k ) approximation due to a mistake in prior work. ) Our approach is based on two main ingredients. First, we derive an approximation preserving reduction to the Group Steiner Tree on Trees with Dependency Constraint (GSTTD) problem. Compared to the classic Group Steiner Tree on Trees problem, in GSTTD we are additionally given some dependency constraints among the nodes in the output tree that must be satisfied. The GSTTD instance has quasi-polynomial size and logarithmic height. We remark that, in contrast, Zelikovsky’s heigh-reduction theorem [Algorithmica’97] used in all prior work on DST achieves a reduction to a tree instance of the related Group Steiner Tree (GST) problem of similar height, however losing a logarithmic factor in the approximation ratio. Our second ingredient is an LP-rounding algorithm to approximately solve GSTTD instances, which is inspired by the framework developed by [Rothvob, Preprint’11; Friggstad et al., IPCO’14]. We consider a Sherali-Adams lifting of a proper LP relaxation of GSTTD. Our rounding algorithm proceeds level by level from the root to the leaves, rounding and conditioning each time on a proper subset of label variables. The limited height of the tree and small number of labels on root-to-leaf paths guarantee that a small enough (namely, polylogarithmic) number of Sherali-Adams lifting levels is sufficient to condition up to the leaves. We believe that our basic strategy of combining label-based reductions with a round-and-condition type of LP-rounding over hierarchies might find applications to other related problems.
An Optimal Itinerary Generation in a Configuration Space of Large Intellectual Agent Groups with Linear Logic

A group of intelligent agents which fulfill a set of tasks in parallel is represented first by the tensor multiplication of corresponding processes in a linear logic game category. An optimal itinerary in the configuration space of the group states is defined as a play with maximal total reward in the category. New moments also are: the reward is represented as a degree of certainty (visibility) of an agent goal, and the system goals are chosen by the greatest value corresponding to these processes in the system goal lattice.
Discovering and Linking Spatio-Temporal Big Linked Data

The growing number of spatiotemporal datasets is an essential driver for bio-economy. Interoperability is needed to ensure efficient use of these data and had been addressed by standardization institutions, such as OGC and AIMS. Both of them promote the use of Semantic Web standards (e.g., GeoSPArql) as one pillar for interoperability [1]. A significant challenge to strengthen the utility of Semantic Web approaches is linking. Its central goal in the context of spatiotemporal datasets is the (semi-automatic) discovery of geospatial referents, such as events, areas, and places which are not yet linked or georeferenced. While the linking task is intrinsically challenging, it is especially resource- and time-consuming when processing and linking Semantic Big Data. This paper will demonstrate an approach which improves and automates linking Semantic Big Data and show its potential usage for bio-economy.
Noncoding transcription influences the replication initiation program through chromatin regulation

In eukaryotic organisms, replication initiation follows a temporal program. Among the parameters that regulate this program in Saccharomyces cerevisiae, chromatin structure has been at the center of attention without considering the contribution of transcription. Here, we revisit the replication initiation program in the light of widespread genomic noncoding transcription. We find that noncoding RNA transcription termination in the vicinity of autonomously replicating sequences (ARSs) shields replication initiation from transcriptional readthrough. Consistently, high natural nascent transcription correlates with low ARS efficiency and late replication timing. High readthrough transcription is also linked to increased nucleosome occupancy and high levels of H3K36me3. Moreover, forcing ARS readthrough transcription promotes these chromatin features. Finally, replication initiation defects induced by increased transcriptional readthrough are partially rescued in the absence of H3K36 methylation. Altogether, these observations indicate that natural noncoding transcription into ARSs influences replication initiation through chromatin regulation.
BFAST Explorer: An Effective Tool for Time Series Analysis

The use of remote sensing images has been broadly employed over the past decades in order to detect and investigate temporal changes on the Earth surface. This is one of the main goals of the widely used Breaks For Additive Season and Trend (BFAST) method. In this paper, we introduce the BFAST Explorer, an effective open-source tool for time series analysis based on BFAST, which is ready to be used at a personal webpage. We present its functional and architectural design, as well as a common usage scenario. Moreover, this tool was well received by the target community, notably by its recently integration into a private cloud computing platform.
Urban TanDEM-X Raw DEM Fusion Based ON TV-L1 and Huber Models

Recently, the TanDEM-X DEM has been produced as a global DEM with unprecedented relative accuracy. One important step of the chain of global DEM generation is to mosaic multiple raw DEM tiles by DEM fusion methods to reach the best possible target accuracy. Currently, Weighted Averaging (WA) is used as a fast and simple method for TanDEM-X raw DEM fusion in which the weights are computed from height error maps delivered from the Interferometric TanDEM-X Processor (ITP). In this paper, we investigate the efficiency of variational models such as TV-L1 and Huber model for the TanDEM-X raw DEM fusion task in comparison to WA. The results illustrate that using variational models can improve the quality of DEM fusion outputs especially for areas with high-frequency contents and more complex morphological features like urban areas. Using variational models could improve the DEM quality by up to about 1m.
Effects of eccentric versus concentric contractions of the biceps brachii on intracortical inhibition and facilitation

Differences in the neural mechanisms underpinning eccentric (ECC) and concentric (CON) contractions exist; however, the acute effects of fatiguing muscle contractions on intracortical and corticospinal excitability are not well understood. Therefore, we compared maximal ECC and CON contractions of the right biceps brachii (BB) muscle for changes in corticospinal excitability, short‐ (SICI) and long‐interval intracortical inhibition (LICI) and intracortical facilitation (ICF) up to 1 hour post‐exercise. Fourteen right‐handed adults (11 M/3F; 26.8 ± 2.9 year) undertook a single session of 3 sets of 10 maximal ECC or CON contractions (180‐second rest between sets) on an isokinetic dynamometer (40°/s) separated by 1 week, in a randomized crossover study. Maximum voluntary isometric contraction torque (MVIC), maximal muscle compound waves (MMAX), and motor‐evoked potentials elicited through transcranial magnetic stimulation (TMS) were recorded via surface electromyography from the right BB. MVIC decreased (P < 0.001) immediately after ECC and CON contractions similarly, but the decrease was sustained at 1 hour post‐ECC contractions only. MMAX was reduced immediately (P = 0.014) and 1 hour post‐exercise (P = 0.019) only for ECC contractions. SICI and ICF increased immediately after ECC and CON contractions (P < 0.001), but LICI increased only after ECC contractions (P < 0.001), and these increases remained at 1 hour post‐ECC contractions only. These findings suggest that ECC contractions induced a longer‐lasting neuromodulatory effect on intracortical inhibition and facilitation, which could indicate a central compensatory response to peripheral fatigue.
Digestive Responses to Fortified Cow or Goat Dairy Drinks: A Randomised Controlled Trial

Fortified milk drinks are predominantly manufactured from bovine (cow) sources. Alternative formulations include those prepared with hydrolysed bovine milk proteins or from alternate bovidae species, such as caprine (goat) milk. Currently, there is little data on protein digestive and metabolic responses following ingestion of fortified milk drinks. To examine the digestive and metabolic responses to commercially-available fortified milks, young adults (n = 15 males: 15 females), in a randomised sequence, ingested isonitrogenous quantities of whole cow-protein (WC), whole goat-protein (WG), or partially-hydrolysed whey cow-protein (HC), commercial fortified milks. Plasma amino acid (AA) and hormonal responses were measured at baseline and again at 5 h after ingestion. Paracetamol recovery, breath hydrogen, and subjective digestive responses were also measured. Postprandial plasma AA was similar between WC and WG, while AA appearance was suppressed with HC. Following HC, there was a negative incremental AUC in plasma branched-chain AAs. Further, HC had delayed gastric emptying, increased transit time, and led to exaggerated insulin and GLP-1 responses, in comparison to whole protein formulas. Overall, WC and WG had similar protein and digestive responses with no differences in digestive comfort. Contrastingly, HC led to delayed gastric emptying, attenuated AA appearance, and a heightened circulating insulin response.
Simulation of Isar Motion Compensation for Moving Targets Based on Particle Swarm Optimization

In inverse synthetic aperture radar (ISAR) imaging, the imaging results can be affected by the unexpected target motions. This results in a blurry and unrecognizable image. The motion parameters estimation is a compensation method to improve the ISAR image refocusing and quality. In this study, the backscattered echo signals are simulated by the linear geometry system of ISAR moving targets. The entropy of ISAR image is used as a criterion to evaluate the image quality. Furthermore, this entropy measure can be treated as a cost function of the particle swarm optimization (PSO) method and minimized by PSO to improve the quality of ISAR images. The experimental results showed that our proposed PSO motion estimation approach to entropy minimization for ISAR imaging can not only efficiently improve the estimation capability of motion parameters, but also significantly achieve a better performance of ISAR image refocusing.
Winter Wheat Yield Estimation with Ground Based Spectral Information

Timely and accurate knowledge of crop yield information is of great significance for guiding agricultural production, formulating agricultural policies and controlling the balance of food supply-demand. In the process of yield formation, spectral analysis technology, as a fast and non-destructive method, has been widely applied to detect the photosynthetic capacity indexes, which is highly correlative with the final crop yield. In this study, the spectral information of winter wheat leaves was obtained in three important phenological phases. The two-dimensional (2D) correlation spectrum analysis modified by the mutual information (MI) was then brought in, and the dynamic spectra were got by using crop yields as the perturbation quantity. After the analysis of the photosynthesis mechanism and 2D synchronous correlation spectra in three periods, three groups of wavebands were selected as the sensitive spectral information to the final yields. The principal component analysis (PCA) was conducted among the acquired wavebands. The crop yield was forecasted by the principal components from the selected wavelengths in different phenological phases. PLS model, the model based on neural network and the neural network prediction model optimized by the genetic algorithm were established separately. After the comparison, the BP Neural network model optimized by the genetic algorithm got a significant improvement in yield estimation accuracy. The results offered the rapid, convenient and valuable guidance for the agricultural production.
Systematic parsing of X.509: Eradicating security issues with a parse tree

X.509 certificate parsing and validation is a critical task which has shown consistent lack of effectiveness, with practical attacks being reported with a steady rate during the last 10 years. In this work we analyze the X.509 standard and provide a grammar description of it amenable to the automated generation of a parser with strong termination guarantees, providing unambiguous input parsing. We report the results of analyzing a 11M X.509 certificate dump of the HTTPS servers running on the entire IPv4 space, showing that 21.5% of the certificates in use are syntactically invalid. We compare the results of our parsing against 7 widely used TLS libraries showing that 631k to 1,156k syntactically incorrect certificates are deemed valid by them (5.7%--10.5%), including instances with security critical mis-parsings. We prove the criticality of such mis-parsing exploiting one of the syntactic flaws found in existing certificates to perform an impersonation attack.
A Unified Perspective of Evolutionary Game Dynamics Using Generalized Growth Transforms

In this paper, we show that different types of evolutionary game dynamics are, in principle, special cases of a dynamical system model based on our previously reported framework of generalized growth transforms. The framework shows that different dynamics arise as a result of minimizing a population energy such that the population as a whole evolves to reach the most stable state. By introducing a population dependent time-constant in the generalized growth transform model, the proposed framework can be used to explain a vast repertoire of evolutionary dynamics, including some novel forms of game dynamics with non-linear payoffs.
Scale calibration for high-dimensional robust regression

We present a new method for high-dimensional linear regression when a scale parameter of the additive errors is unknown. The proposed estimator is based on a penalized Huber $M$-estimator, for which theoretical results on estimation error have recently been proposed in high-dimensional statistics literature. However, the variance of the error term in the linear model is intricately connected to the optimal parameter used to define the shape of the Huber loss. Our main idea is to use an adaptive technique, based on Lepski's method, to overcome the difficulties in solving a joint nonconvex optimization problem with respect to the location and scale parameters.